MICROWAVE IMAGING TECHNIQUES FOR NONDESTRUCTIVE EVALUATI ON: SIMULATION AND EXPERIMENTAL STUDIES b y JARVIS WARD HILL B.S., Colorado State University Fort Collins 2009 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Master of Science Electrical Engineering 2014
ii 2014 JARVIS WARD HILL ALL RIGHTS RESERVED
iii This thesis for the Master of Science degree by Jarvis Ward Hill has been approved for the Electrical Engineering Program by Stephen Gedney, Chair Yiming Deng, Advisor Tim Lei Mark Golkowski November 21, 2014
iv Hill, Jarvis Ward (M.S. Electrical Engineering) Microwave Imaging Techniques for Nondestructive Evaluation: Simulation and Experimental Studies Thesis directed by Assistant Professor Yiming J. Deng ABSTRACT Microwave imaging is an emerging area of research. Due to its low cost and simple system setup s m icrowaving imaging system ( s ) (MIS ) are growing as alternatives to more expensive X ray imaging systems. This thesis provides background on microwave It highlights some of the design considerations and challenges that can impact the development of an MIS It also provides suggestions on how to achieve an MIS with higher res olution, how to eliminate noise within acquired images and how to optimize sc anning execution times without generating excessive sample vibrations. The ancillary equipment of an MIS should be catered to the imaging application. This thesis exposes one to the physical development process of the LEAP Near field Microwave Imaging System (NFMWIS) It documents the areas and stages of the system development and how s imulation was used to confirm physical behavior of the system components prior to purchase and or fabrication. Develop ment efforts show that t here is high impo rtance in th e waveguide design of the MIS and a strong need to optimize the relationship between the MIS hardware and software. The waveguide design should be conducive to the size, shape and geometry of the desired objects to be imaged. Waveguides with large apertures are useful in applications that require low resolution. Waveguides with small apertures hel p confine the EMF radiation to localized region s on the sample under test and help achieve high resolution. An MIS is not efficient if it
v requires large scanning e xecution times in order image an object under test but more importantly image resolution should not be sacrificed for fast scanning times. Ultimately, there is not a single solution when developing a MIS all factors must be experimentally optimized by the design engineer(s). The form and content of this abstract are approved. I recommend its publication. Approved: Yiming J. Deng
vi DEDICATION I would like to dedicate this thesis to the Hill family who made the sacrifices in order to allow me to complete this task Many times it takes a village to help someone get to encouragement and belief. They inspire me to be a better pers on as each day passes ; they teach me to lead and not follow; and they have instilled in me a hunger to dream big!
vii ACKNOWLEDGEMENTS I must thank my program advisor Dr. Yiming Deng for his limitless spirit of humility, understanding and encouragement My graduate tenure has truly been a journey and he has guided me every step of the way I would like to thank the Laboratory of Electromagnetic and A coustic Imaging and Prognostics Team (LEAP) for their contribution in developing the Near Field Microwave Imaging System (NFMWIS) Special thanks go es t o Xiaoye Chen and Salem Egdaire. Salem thank you for your collaboration with develop ing the NFMWI S phase detection approach and for al ways challenging my ideas and experiments during those many late nights in the lab. Xiaoye Chen thank you for your significant contributions on the development of the NFMWIS scanning tip I would also like to thank our research collaborators at Arizona State University, Dr. Yo ngming Liu and Sahil Jain I would like to offer huge kudos and thanks to James Merri t t, R&D Manager for the U.S. Department of Transportation for the funding that allows this program to continue to develop.
viii TABLE OF CONTENTS CHAPTER I INTRODUCTION ................................ ................................ ................................ ........... 1 1.1 OVERVIEW ................................ ................................ ................................ .............. 1 1.2 THESIS SCOPE ................................ ................................ ................................ ........ 4 1.3 MOTIVATIONS & OBJECTIVES ................................ ................................ .......... 6 II REVIEW OF NEAR FIELD MICROWAVE IMAGING TECHNIQUES ................. 10 2.1 HISTORY OF MICROWAVE IMAGING ................................ ............................ 10 2.2 NEAR FIELD & FAR FIELD EMF RADIATION ................................ ............... 11 2.3 TIME HARMONIC ELECTROMAGNETIC FIELDS ................................ ......... 14 2.3.1 Maxwell Equations ................................ ................................ ........................... 14 2.3.2 Constitutive Equations ................................ ................................ ...................... 16 2.3.2 Wave Equation ................................ ................................ ................................ 17 2.3.3 Helmholtz Wave Equation ................................ ................................ ................ 17 2.3.4 EM Wave Scattering ................................ ................................ ......................... 19 2.4 GENERAL NEAR FIELD MICROWAVE IMAGING TECHNIQUES ............... 21 2.4.1 Open ended Waveguide Techniques ................................ ................................ 21 2.4.2 Near Field Scanning Tip Techniques ................................ ............................... 28 2.4.3 Near field Phase Detection Approach ................................ .............................. 37 2.5 NEAR FIELD MEDICAL IMAGING APPLICATIONS ................................ ...... 40 2.6 SUMMARY ................................ ................................ ................................ ............ 44 III OPTIMIZATION OF THE LEAP NFMW IMAGING SYSTEM ............................. 47 3.1 MICROWAVE IMAGING PROBLEMS ................................ ............................... 47 3.1.1 Constructing Valid Numerical Models ................................ ................................ 47 3.1.2 Real time NFMW Imaging ................................ ................................ ............... 47 3.1.4 Phase Extraction Challenges ................................ ................................ ............ 49 3.2 LEAP NFMWI SYSTEM OVERVIEW ................................ ................................ 50 3.2.1 Approach 1 LEAP NFMWIS Voltage Amplitude Detection System .............. 51 3.2.2 Approach 2 LEAP NFMWI Phase Detection System ................................ ...... 51 3.3 LEAP NFMWIS OPTIMIZATION METHODS ................................ .................... 52 3.3.1 System Synchronization ................................ ................................ ................... 53 3.3.2 Scan Execution Time ................................ ................................ ........................ 53 3.3.3 Antenna (Waveguide) Design ................................ ................................ .......... 61 3.3.4 Image Resolution Techniques ................................ ................................ .......... 69 3.4 LEAP NFMW PHASE DETECTION APPROACH ................................ .............. 72 3.4.1 Background Information ................................ ................................ ................... 73 3.4.3 Analog Mixing Phase Detection Approach ................................ ...................... 76 3.5 LEAP NFMWI PHASE DETECTION SYSTEM ................................ .................. 86
ix 3.6 INITIAL LEAP NFMWI PHASE DETECTION ................................ ................... 92 3.6.1 LEAP NFMWI Phase Detection Measurement Method ................................ .. 93 3.6.2 Initial Phase Detection Scan Results ................................ ................................ 94 3.7 SUMMARY ................................ ................................ ................................ ............ 98 IV CONCLUSION ................................ ................................ ................................ ........ 100 4.1 FUTURE WORK ................................ ................................ ................................ .. 100 4.1.1 Numerical Modeling of LEAP NFMWIS Waveguide ................................ ... 100 4.1.3 Compressed Sensing Reconstruction ................................ .............................. 110 4.2 CLOSING REMARKS ................................ ................................ ......................... 111 4.3 CONCLUSION ................................ ................................ ................................ ..... 116 REFERENCES ................................ ................................ ................................ ............... 118 APPENDIX A: MATLAB NFMWIS Scanning Code (s can.m ) ................................ ..... 121 APPENDIX B: LEAP NFMWIS Scanning Algorithm ................................ ................. 126
x LIST OF TABLES TABLE 3.1 IF Voltage Frequency Harmonics ................................ ................................ .............. 92
xi LIST OF FIGURES FIGURE 1.1: Some common transmission line waveguides: (a) Two conductor line; (b) coaxial line; (c) shielded strip line  ................................ ................................ ............................. 3 1.2: Some common hollow pipe waveguides: (a) Rectangular; (b) circular guide; (c) ridge guide  ................................ ................................ ................................ .............................. 4 2.1: EMF B oundary Regions  ................................ ................................ ..................... 12 2.2: Near field and Far Field Radiation Behavior  ................................ ................... 13 2.4: LEAP NFMWIS Generation I 1D scan of Hole in a Concrete Sample .................... 22 2.5: Bois Microwave Imaging System for Concrete Cure State Inspection ..................... 23 2.6: Open ended Coaxial Line Terminated by a Two Layered Medium .......................... 26 2.7: Magnitude of th e reflection coefficient vs. target distance (left); Phase of the reflection coefficient vs. target distance (right) ................................ ................................ 27 2.8: Schematic of Microstrip Resonator as Used in a Microwave Evanescent Probe ...... 30 2.9: Measured |S 11 | of channelized coplanar waveguid e probe both with and without a high resistivity silicon sample near the probe tip ................................ .............................. 31 2.10: Wang Coaxial Tip Fabrication Process ................................ ................................ ... 32 2.11: Micrograph of a Wang Fabricated Silicon Tip ................................ ....................... 33 2.12: Scan of a 20 pm diameter wire along the x and y axis using CCPW evanescent microwave probe ................................ ................................ ................................ ............... 34 2.13: Circuit Schematic for SNMM Scan ................................ ................................ ......... 35 2.14: Simultaneous SNMM images by the coaxial tip. (left) AFM internal sensor image; (right) Microwave amplitude image ................................ ................................ ................. 36 2.15: Zhu Spintronic Phas e Detection MIS ................................ ................................ ..... 38 2.16: Near field Phase Difference Measurements of Liquid Filled PMMA Grating: (a) Grating filled with isopropyl alcohol; (b) Grating filled with water ................................ 39 2.17: 3 D Tomographic MIS Examination Chamber: (left) 3 D representation of the tomographic chamber; (right) Assembled antenna hardw are ................................ ........... 42 2.18: MR Imaged tumor in right breast: (left) Tumor Axial View (right) Tumor axial View After Baseline Subtraction ................................ ................................ ...................... 43 2.19: 3 D Permittivity Breast Image from 3 D Tomographic MIS ................................ 43 2.20: Generic Microwave Imaging System  ................................ .............................. 45 3.1: Block Diagram of LEAP NFMWI Voltage Amplitude Detection System ................ 51 3.2: Block Diagram of LEAP NFMWI Phase Det ection System ................................ ..... 51 ................................ ................................ .. 52 3.4: Flow Diagram of Initial LEAP NFMWIS Raster Scan Algorithm ............................ 56 3.5: Flow Diagram of Optimized LEAP NFMWIS Raster Scan Algorithm ................... 58
xii 3.6: LEAP NFMWIS Open Ende d WG Scan: (left) Calibration sample; (right) Microwave Image ................................ ................................ ................................ ............. 62 3.7: LEAP NFMWIS Open Ended WG Scan with SA DAQ Device ............................... 63 3.8: LEAP NFMWIS CTA waveguide: (left) waveguide tip and (right) signal spliter and amplification (annotate picture) ................................ ................................ ........................ 64 3.9(a): LEAP NFMWIS Scan Results Calibration sample CTA scan results ................... 65 3.9(b): LEAP NFMWIS Scan Results Open ended WG Scan Results ............................ 65 3.10: Copper Probe Tips ................................ ................................ ................................ .. 66 3.11: CPT Return Loss ................................ ................................ ................................ ..... 67 3.12: Experimental setup with CPT ................................ ................................ .................. 68 3.13: Scan result comparison between CPT (left) and CTA (right) ................................ 68 3.14: Histogram of Gen. I Rx Amplitude Data Acquired wit h SA ................................ ... 70 3.15: MATLAB Thresholding Algorithm ................................ ................................ ........ 71 3.16: Binary Thresholding Results: (left) Voltage Amplitude Image using LEAP NFMWIS Generation I; (right) Image after applying optimized binary thresholding algorithm ................................ ................................ ................................ ........................... 72 3.17: NFMWI Phase Measurement Simulation: Rx Signal (right); Scalar Spectrum Analyzer Measurement (left) ................................ ................................ ............................ 74 3.18: NFMWIS Phase Measurement Simulation: 90 o Phase Shift Rx Signal (left); Scalar Spectrum Analyzer Measurement (right) ................................ ................................ .......... 75 3.19: Output Signal Power Displayed on the Function Generator ................................ .... 76 3.20: Schematic of Product Detector (Analog Multiplier)  ................................ ....... 76 3.21: Plot of Tx and Rx and their frequency spectrums ................................ ................... 78 3.22: Tx and Rx Product Signal and frequency spectrum ................................ ............... 79 3.24: V OGX vs. Tx and Rx Phase Difference ................................ ................................ .... 80 3.25: Double balanced Mixer Phase Detection Circuit ................................ .................... 80 3.26: Screenshot of Double Balanced Mixer Built with LTspice ................................ .... 82 3.27: LO and RF Phase Detector Input Signals ................................ ............................... 82 3.28: LO Induced Signal into Phase Detector Circuit ................................ ...................... 83 3.29: Phase Detector IF output without Low pass Filter ................................ ................. 83 3.30: Spectrum of Mixer IF Output Voltage ................................ ................................ ..... 84 3.31: Phase Detector IF output with Low pass Filter ................................ ...................... 84 3.32: Double balanced Mixer Phase Difference Measurement ................................ ....... 85 3.33: Detected Phase Differences Actual vs. Calculated ................................ ................. 85 3.34: MATLAB Phase Detector Product Vol tage Results vs. LTspice Double Balanced Mixer IF Voltage ................................ ................................ ................................ ............... 86 3.35: Diagram of Mixer BW measurement setup ................................ ............................. 88 3.36 Forward voltage gain from the RF input to the IF output ................................ ......... 89 3.37: RF Mixer IF Voltage Experiment Setup ................................ ................................ 90 3.38: MATLAB, LTsp ice and SM1717 RF Mixer IF Voltage Response ......................... 90
xiii 3.39: MATLAB, LTspice and SM1717 RF Mixer Phase Difference Approximations ... 91 3.43: SA CXA N9000A display with 3GHz and 1 GHz fed to the mixer inputs ............. 92 3.43: LEAP NFMWI Phase Detection System ................................ ................................ 93 3.44: Mid size Triangle on Aluminum Calibration Sample ................................ ............ 93 3.45: Phase Detection Feasibility Scan of Mid size Triangle ................................ ........... 95 3.46: Image of Calculated Phase Difference ................................ ................................ ..... 96 3.47: Image of Mixer IF Voltage Measurements ................................ .............................. 96 3.48: Image of Calculated Phase Diff erence ................................ ................................ ..... 97 3.49: Image of Mixer IF Voltage Output Default imagesc Color Map ............................ 97 3.50: Image of Calculated Phase Difference Default imagesc Color Map ....................... 98 4.1: Yee Cell Mesh Grid ................................ ................................ ................................ 102 4.2: EMFs at material discontinuity: E field at PEC boundary ( left ): H field at PMC boundary ( right ) ................................ ................................ ................................ .............. 103 4.3: FDTD Parallel Plate Computational Parameters ................................ .................... 104 4.2: Sinusoidal Pulse Stimulus ................................ ................................ ........................ 106 4.3: Continuous Wave Pulse Stimulus ................................ ................................ ............ 106 4.4: 2D and 3D Ez Field Due to Sinusoidal Pulse Stimulus ................................ ........... 109 4.5: 2D and 3D Ez Field Due to Continu ous Pulse Stimulus ................................ ......... 109
1 CHAPTER I INTRODUCTION 1.1 OVERVIEW Microwave energy was exposed to the average consumer when the microwave oven (microwave) became a common household appliance in the 1970s. The oven generates electromagnetic radi ation at micrometer wavelengths; this micrometer radiation is known as microwaves. The descriptive term microwaves is used to describe electromagnetic waves with wavelengths ranging from 1 cm to 1 m. The corresponding frequency range is 300 MHz up to 30 GHz for 1 cm wavelength waves  When a material is exposed to microwave radiation molecules within the material interact and alter how the waves transmit through the material How microwave radiation travels through an object is described by the materials dispersive and dielectric properties Material d ispersion describes how the phase of the microwave radiation propagates through the material. The dielectric properties of an object are described by the magnetic permeability and electric permittivity of the material. In electromagnetism, permeability is the measure of the ability of a material to support the formation of a magnetic field within itself. On the other hand, Permittivity is the measure of the ability of a material to support the formation of an electric field. Materials are anisotropic meaning the dielectric structure of a material is inhomogeneous and the permittivity and or permittivity are not constant throughout If the dielectric structure was constant the material would be considered isotropic and or homogenous U nlike X rays where the rad iation wavelength is much smaller than the object, EM radiation at microwave frequencies no longer travel s in a straight path as the
2 size of the objects is comparable to the wavelength  This phenomenon allows microwaves to exploit material anisotropies. When an object is exposed to a microwave field, the field radiates within the material and reflect s or scatter s /disperse s when the field come s in contact with the anisotropic molecules within the object materia l. The amplitude of the reflected or scattered field can be quantified and measured by employing microwave imaging techniques. The se techniques commonly quantify the amplitude of radiation by measuring the reflection coefficient or the ratio of the reflected microwaves to the transmitted microwaves at a localized region on the object The larger the dielectric differences within a material the larger the amplitude of the reflected or scattered radiation. During World War I1, microwave engineer ing was almost synonymous with radar (RAdio Detection And Ranging) engineering There was a great incentive given to the development of microwave systems due to the need for high resolution radar capable of detecting and locating enemy planes and ships. Even today radar, in its many varied forms, such as missile tracking radar, fire control radar, weather detecting radar, missile guidance radar, airport traffic control radar, etc., represents a major use of microwave frequencies  This use arises predominantly from the need to have antennas that will radiate all the transmitter power into a narrow pencil like beam similar to that produced by an optical searchlight  The ability of an antenna t o concentrate radiation into a narrow beam is limited by diffraction effects, which in turn are governed by the relative size of the radiating aperture in terms of wavelengths  Research has shown that the ability to concentrate radiation enables the various field amplitudes across a material to be imaged using microwave imaging systems (MIS) These systems can generate images
3 of a materials dielectric tomography allowing engineers and scientist to evaluate hidden or embedded flaws within the object structure  In microwave applications waveguides are the most critical component of a microwave system because the y couple the antenna to the microwave energy source. Waveguides are transmission mediums that are responsible for transmitting as much microwave energy from the source to the object under test. Ideally a waveguide would transfer all the energy from the source to an antenna without causing any signal attenua tion. Due to the confined geometries of the waveguide walls transverse modes (TEM) are generated within the guide due to the boundary conditions the waveguide structure imposes on an EMF. The dominate mode or TEM containing the most microwave energy is the EMF oscillating at the lowest frequency S labs of material with a specified thickness and dielectric property are often inserted into the waveguide in order to act as a low pass filter removing the propagation of these unwanted higher order TEM s Rem oving unwanted TEM s can increase SNR within a microwave imaging application and increase imaging resolution. Figures 1.1 and 1.2 show some typical waveguide geometries. Figure 1.1: Some common transmission line waveguides : (a) Two conductor line; (b) coaxial line; (c) shielded strip line 
4 Figure 1.2 : Some common hollow pipe waveguides: (a) Rectangular; (b) circular guide; (c) ridge guide  There is a wid e variety of use for microwave imaging applications and t he waveguide and or antenna of a MIS is dictated by the specificities of the application ( i.e. the size and geometry of the object to be imaged t he required imaging resolution, the imaging environment and what the system needs to measure) Research has shown that open ended rectangular waveguides are useful in applications that warrant the imaging of large objects and require low imaging resolution   Research shows that open ended coaxial waveguides localize EMF on a sample under test and is useful for applications requiring sub cm resolution  Research suggests that MIS requiring high resolution (greater than sub mm) should be implemented with a waveguide terminated to a tapered tip antenna   1.2 THESIS SCOPE Microwave NDE imaging is a science that provides a quick and nondestructive way to classify properties of a material. Factors in choosing the right nondestructive evaluation (NDE) technique depend on the type of material, as well as, the size, orientation and location of the defect  Whether defects are located on the surface or internally also affects the selection of the technique  The geometric shape of a material under test also determines which NDE technique to use  All these factors determine the type
5 and equipment of a MIS. When constructing a MIS there is no ideal solution. The solution should be optimized based on the material type, geometry, flaw detection r esolution and location of the flaws that need to be imaged  the final system is optimized through experimentation and evaluation because the interaction of materials with electromagnetic fields (EMF) is impalpable. The interaction between EMF and materials may appear ambiguous but researchers have found ways to quantify it. Near field microwave imaging techniques have been employed to classify the material mixture of concrete by scanning the material at pre determined locations and quantifying the reflected field amplitude wit h a network analyzer (NA)  Techniques have also been used to achieve microwave breast imaging  and to produce 3 D tomographic microwave images for breast cancer detection  All of these techniques have one thing in common; they are carried out with near field microwave imaging techniques. Many microwave imaging techniques exist but like the MIS, the technique depends on the material under test and th e information desired. Chapter 2 introduces near field microwave imaging and its interaction with various object types. The chapter will also perform literature review highlighting different strategies to acquire quantized images of material properties u sing MIS (e.g. conductivity, dielectric constant or polarization)  The most important component of the MIS is the waveguide. The size, shape and lift off distance of the waveguide orchestrates how the microwaves interact with a material under test  Research has shown that the emitted microwave radiation and lift off distance of the material can be optimized through numerical modeling techniques before MIS construction  The speed and resolution of a microwave imaging technique are two substantial
6 factors that should be considered when developing a MIS. Chapter 3 shows the NFMWIS optimization methods and results The chapter presents a system overview, phase detection approach to microwave imaging, automated data acquisition algorithms using MATLAB and initial efforts of MIS image enhancement techniques. Chapter 4 provides a conclusion and summary of future wor k The future work presented in Chapter 4 provides exposure to the initial efforts made by the LEAP Team to g auge the response of an open ended waveguide to EMF radiation using finite difference time doma in (FDTD) modeling techniques. FDTD modeling is us eful because it provides an approximation of the EMF behavior inside the waveguide. The modeling efforts in Chapter 4 are incomplete and are a part of the continuous improvement efforts of the LEAP MIS 1 .3 MOTIVATIONS & OBJECTIVES Richard Hamming Everyone knows that death is imminent. Witnessing the death of a close relative really puts this into perspective. Death can be graceful when it takes a ninety year old vibrant, energetic and loving fifty year why represented denial and the falling tears represented healing. Of course the onset of breast cancer cannot be completely avoided but it can be mitigated through regularly scheduled examinations. Microwave imaging is progressing considerably in the areas of early breast cancer detection    The effectiveness of imaging contrasts in objects
7 containing sacks of fluid will hopefully translate into imaging tissue with abnormal coagulates, such as tumors. that at a time like this you cannot be called forth? Wyclef Jean The nondestructive diagnostic possibilities of microwave imaging are countless but the t e chniques that are most impactful are the ones that could potentially save lives. Like the microwave oven easing the life of the 70s domestic housewife, saving lives matter. Lives impact other lives. One more day, hour, second for a father, mother, sister, brother holds an unquantifiable amount of potential energy. Potential energy that is transferred from one life to the next; in the right time applied and transformed into kinetic energy to change the world. Microwave imaging requires scientists to research, analyze and evaluate in order to arrive at optimal solutions. The challenge, ambiguity, lack of rules and freedom to create things is what makes science and engineering fun. The struggle to create superior imaging diagnostics can lead to fewer l ives lost to breast cancer, airplane crash fatalities, and even hypothermia  The objectives of the LEAP research group is to develop a near field microwave imaging technique using open ended waveguide probes that offer m icron to sub mm spatial resolution and provides non contact, one sided and near real time measurement capabilities at the same time. This imaging technique is an effort to satisfy the US DOT CAAP supported research project for the residual strength and rem aining useful life (RUL) analysis of pipeline material s
8 The USDOT CAAP supported research is a collaborative effort between the USDOT, University of Colorado Denver and Arizona State University that aims to develop a new hybrid sensing technique that can identify and proactively characterize injurious pipe body with superior resolution and high sensitivity. The detection results from the proposed nondestructive evaluation (NDE) sensing methodology wi ll be integrated with probabilistic methods and mechanical analysis for accurate time dependent reliability analysis. Effective reliability analysis using NDE techniques requires a fused information framework that integrates the residual strength calculat ion, uncertainty quantification, propagation analysis, and Bayesian updating. If successful, the pipeline failure can be significantly reduced with this innovative pipeline defects diagnosis and prognosis approach. The overall objectives of this coll aborative research are two fold: diagnosis find existing damage at the earliest stage before it becomes failure critical; and prognosis accurately predict the remaining strength and RUL of pipelining components. The effort will last 27 months and will est ablish a prototype concept, composed of mathematical modeling of the near field microwave and acoustic interaction, experiment al measurements, feature extraction and pipeline prognosis based on damage quantification and uncertainty analysis. Building upon this concept, a sensor prototype with the integrated diagnosis and prognosis capabilities will be designed, fabricated, and tested at the last phase of this project. The works presented in this thesis are personal efforts to contribute to the dia gnosis of existing pipeline damage through the develop ment and optimiz ation of the LEAP near
9 field microwave imaging technique ( LEAP NFMWIS ) The objectives of this thesis are three fold: 1. Development of an efficient and real time data acquisition approac h for NFMW imaging. 2. Development of a phase extraction methodology for NFMW imaging. 3. Development and validation of a numerical model for near field microwave imaging.
10 CHAPTER II REVIEW OF NEAR FIELD MICROWAVE IMAGING TECHNIQUES 2.1 HISTORY OF MICROWAVE IMAGING The existence of microwaves was made clear in the 1860s with the emergence of four equations that describe the theory of electrodynamics. These equations are known as the Maxwell Equations, named after the great physic displacement current term in 1861. This correction aided in formulating the classical theory of electromagnetic radiation. In 1865 Maxwell p ublished A Dynamical Theory of the Electromagnetic Field which demonstrated that electric and magnetic fields travel through space as waves moving at the speed of light. He also demonstrated that light behaves like the wave motion of electric and magnetic fields which led to the prediction of radio waves. The works that supported this paper enabled him to derive the electromagnetic wave equation (wave equation) which is one of the most fruitful contributions to physics since Newto led to the development of Radio Detection and Ranging or RADAR which can be considered the forefather of microwave imaging. The early developments of microwave imaging techniques started with advancements made in microwave microscopy. Microwave imaging first achieved sub mm resolution when Edward Synge proposed using an opaque screen with a small sub wavelength diameter hole (10 nm in diameter), held about 10 nm above the surface of a smooth flat sample. An optically transparent sample was passed just beneath this aperture, and the transmitted light is collected in a point by point scan and raster fashion [ Synge et. al ]
11 Ash and Nichols utilized the Synge geometry of a small aperture (1.5mm diameter) sc anned over a sample with a microwave signal at 10 GHz. Using a quasi optical hemispherical resonator as the detection system, the sample was harmonically distance modulated at a fixed frequency, and the reflected signal was phase sensitively detected to i mprove sensitivity to sample contrast [ Ash and Nichols et. al ] The 1960s marks the first decade when a transmission line was used to radiate microwave fields from the open end of a line (coaxial or waveguide). The fringes of the EMF from the aperture interact with the test sample. Part of the signal is absorbed b y the sample, part of it is stored locally in evanescent and near zone waves, a portion is reflected back up the transmission line and some scatters away as far field radiation  Bryant and Gunn are noted for performing som e of the first experiments with tapered open ended coaxial probes [ Bryant and Gunn et. al ] They made quantitative measurements of semiconductor resistivity by measuring the reflection coefficients produced in a coaxial cable probe with an inner diameter of 1 mm. These early experiments led to some of the most fundamental microwave imaging techniques. 2.2 NEAR FIELD & FAR FIELD EMF RADIATION Electrical engineers define boundary regions to categorize behavior characteristics of electromagnetic fields as a function of distance from the radiating source. These regions are: the "Near Field", "Transition Zone", and "Far Field". The regional boundaries are usually measured as a function of the wavelength. Figure 2.1 sh ows these regions and boundaries.
12 Figure 2.1: EMF Boundary Regions  Imaging in these regions is specific to the required image resolution, material properties and geometry of the sample under test  The near field and far field are regions of the EMF around an object (e.g. transmitting antenna, waveguide, and or probe) or the result of radiation scatterin g off an object (e.g. metal sample). Non radiative near field behaviors of electromagnetic fields dominate close to the antenna or scattering object, w hile electromagnetic radiation far field behaviors dominate at greater distances. The radiative near field (also called the "Fresnel region") covers the remainder of the near field region, from out to the far field or F raunhofer distance. T he far field region is commonly taken to exist at distances from the microwave source that are greater than R = 2D 2 ve from the source  EMF n ear field radiation strength decreases with distance R from the source whereas far field strength decreases with the inverse square of the distance (1/R 2 )  Figure 2.2 displays the radiation behavior of near field EMF and far field EMF.
13 Figure 2.2: Near field and Far Field Radiation Behavior  Near field and far field applications can be implemented in order to create microwave inspection systems. Near field microwave imaging techniques are based on the interaction between evanescent fields generated by an electrically small probe and the mater ial in the close proximity of the probe. Alterations in the material composition or the shape of the object near the probe change the near field distribution. Consequently the amplitude of the scattered/reflected EMF induced in the waveguide of the MIS c hanges. Scanning the probe over an area yields an image corresponding to the material composition around the probe with sub mm wavelength resolution  Far field imaging techniques are not affected by the scattering and ele ctric charge effects that the near field radiation induces on the EMF. The equations describing the fields created about the antenna (near field) can be simplified when imaging in the far field. The far field assumes a large separation between the microw ave source and the device under test. In this region, near field diffraction provides only a minor contribution to the final field so these contributions are dropped. In the far field the EMF radiation becomes plane waves of constant frequency whose wave fronts are constant in phase and amplitude. These simplified distributions have been termed the "far field" and usually have the property
14 that the angular distribution of energy does not change with distance The simplifications of EMF radiation in the f ar field are very useful in engineering calculations Microwave images can be generated in the far field the same way they are generated in the near field, by measuring the amplitude of the reflected EMF. Researchers have been exploiting experimenting with the microwave images that can be generated in these regions. Typical imaging strategies involve generating the topography of a sample by performing a raster scan of the surface area and measuring the amp litude of the signal reflection using a data acquisition device (DAQ). Taking and dielectric properties (permittivity, permeability and conductivity) to be imaged with M IS. The LEAP NFMWIS is a near field imaging system so this thesis will only focus on near field microwave imaging techniques. 2.3 TIME HARMONIC ELECTROMAGNETIC FIELDS 2.3.1 Maxwell Equations Electromagnetics (EM) is a fundamental science essential to understand the basic concepts in physics and electrical engineering. EM theory deals with static, quasi static and moving charges that causes current flow and maintains electromagnetic fields. Unlike circuit theory, object dimensions are comp ara ble to the operating wavelength in electromagnetic field theory and systems are analyz ed using distributed parameters and coupling phenomenon. Electromagnetic field scat tering, propagation, radiation, reception and generation are characterized with the
15 Ma xwell equations are necessary to understand EM radiation and how it scatters when it comes in contact with objects. Using curl, the Maxwell equations in point form can be derived. (2 .1) (2 .2) ( 2 .3 ) ( 2 .4 ) E and H : electric and magnetic fields measured in (V/m) and (A/m) D and B : electric and magnetic flux densities measured in (C/m 2 ) and (Tesla) J : electric current density in (A/m 3 ) v : volumetric electric charge density (C/m 3 ) : permittivity (H/m) and permeability (F/m) The Maxwell equations can d e scribe how EM radiation propagates through time and space. Consider a vector field V given by, ( 2 .5 ) Maxwell proved that EM radiation propagates as time harmonic EM waves. So any component in the vector field can be represented as, (2 .6) (2 .7) Substituting equation 4.7 into equations 4.1 to 4.4 yield s the complex Maxwell equations (2 .8 )
16 (2 .9 ) ( 2 .10 ) ( 2 .11 ) = : angular frequency or the rate of change of the EMF phase 2.3.2 Constitutive Equations In the presence of electromagnetic fields, the stable state of the particles inside a material are altered  The response of the material to an EM field can be approximated using the constitutive equation s  In isotropic media or media where the dielectric properties (i.e. susceptibility, permeability, permittivity and conductivity) are all the same, the continuity equations are, (2 .12) ( 2 .13 ) ( 2 .1 4 ) o and o : free space permittivity (H/m) and permeability (F/m) x e = ( r 1) and x m r 1) : dimensionless electric and magnetic susceptibilities r and r : dimensionless material relative permittivity and permeability : material electrical conductivity (Siemens/m). The constitutive equations show how the structure of a material behaves in the presence of a n EMF is described by a materials susceptibility permeability, permittivity and conductivity. These parameters are used to classify materials as dielectric, magnetic, conductor or semi conductor. In order to understand this phenomenon one must first
17 und erstand how EM radiation propagates in space and then how it propagates when it is obstructed by particles in a material. 2.3.3 Wave Equation The wave equation describes how EM fields propagate through time and space. It is a second order partial differential equation that describes how waves move in time and space. In order to understand the propagation of EM radiation, first consider the wave equation in one dimension, (2.15) c : is the speed of light in a vacuum (maximum speed particles can travel) V : EM vector field (see equation 2.6) Substituting equation 2.7 into 2.15 one can see how the vector field propagates in the x direction in relation to time. (2.16) Using the theory of linear differential equations, the general solution of the vector field V is a linear combination of cosines and sines [linear ODE book]. (2.17) 2.3.4 Helmholtz Wave Equation In order to understand how fields behave within an object, consider a source free dielectric medium or material, the complex Maxwell Equations become,
18 (2.18) (2.19) ( 2.20 ) ( 2.21 ) Taking the equation, ( 2.22 ) vector wave equation, ( 2.23 ) Equations 2.22 and 2.23 are known as the homogeneous Helmholtz Wave Equations. These equations describe how EM radiation propagates in a source free dielectric medium. Like the solution of the one dimensional wave equation, the solution to equation 2.23 will be a linear combination of sine and cosine functions in time (equation 2.17) with angular will de pend on the boundary cond itions implied by the propagation medium.
19 2.3. 5 EM Wave Scattering As mentioned throughout this thesis microwave imaging exploits the dielectric differences within materials in order to generate a microwave tomography of a material under evaluation In the case of NDE this process is non invasive. acquired by illuminating the object from different angles using a penetrating energy source  The mathematical foundations for image reconstruction from projections dates back to  Microwaves are electromagnetic radiation that falls in the range of 30MHz 300GHz. EM radiation at microwave frequencies waves no longer travel in a straight path as the size of the objects is comparable to the wavelength  At these frequencies, the EM radiation undergo es diffraction or scattering when the radiation waves come in contact wi th particles within the material This scattering can be generali zed into two distinct types of transmission or forward scattering and reflection or back scattering. Figure 2.3 shows the schematic illustration of different microwa ve tomography measurement techniques. and the fields are related to the material property by the constitutive equations   An EM wave impinging on a penetrable object undergoes diffraction and multiple scattering within the object resulting in a nonlinear relationship between the measured field and electrical property of the object at the incident frequency  This behavior is known as the inverse scattering effect. Inverse scattering problems aim to reconstruct or estimate the spatial distribution of a materials electrical properties or the scattering potential of the
20 obstacle from scattered f ield measurements  In microwave imaging these field measurements are commonly made with a network analyzer. Figure 2.3 : Commonly used Diffraction tomography setup (a) (b) Transmission or forward scattered (c) Reflection or back scattered.  The scattered field generated from an arbitrary shaped object with permeability r ( r r ( r ) can be obtained by substituting the permeability and permittivity for the arbitrary material into the Helmholtz Wave Equation equation 2.23. (2.24) : the electric field that can be either the E x E y, E z component of the electric vector field Employing separation of variables on equation 2.24 yields   (2.25) k = : wavenumber which describes how the field disperses /travels with in an object for a given radiation wavelength
21 : forcing function that is only a function of time and describes how the radiated EMF within the object material is affected based on the permittivity and permeability at a given location within the object. In the absence of the material dielectric scatterer, only the incident E field ( ) exists everywhere and (2.25 ) reduces to the form, (2.26 ) When the field comes in contact with a material dielectric scat ter er, the field is the sum of the incident E field and the scattered E field from the dielectric scatterer within the material Substituting this E field quantity into (2.25) yields, (2.2 7 ) Equation 2.27 is the inhomogeneous Helmholtz Wave E quation and the total field that is scattered by a penetrable dielectric object The magnitude of this field can be measured by a microwave imaging system in order to generate the dielectric tomography of an object under test. 2 .4 GENERAL NEAR FIELD MICROWAVE IMAGING TECHNIQUES 2.4 .1 Open ended Waveguide Techniques Microwave images can be generated by measuring the amplitude of the reflected voltage from a sample under test. The LEAP Team generated an image from the MIS voltage response to a reflected microwave field from a concrete sample. A hole in the sample was scanned and the resulting image and one dimensional (1D) signal is shown in Figure 2.3. This image was acquired with the LEAP NFMWIS Generation I proposed in C hapter 3
22 Figure 2. 4 : LEAP NFMWIS Generation I 1D scan of Hole in a Concrete Sample The measured signal clearly shows the h ole in the center of the image and captures its 1 inch diameter geometry which matches dimension s of the fabricated sample. For this acquisition, the source frequency was 10 GHz with a scanning step size of 300 micr ons. Sample topographies can also be generated by quantifying the amount of signal reflection from a region on the sample with a network analyzer   These measured reflections can be translated into images based on the voltage standing wave ratio VSWR, permittivity  and conductivity and phase   In the experiment performed by Karl Bois in the paper Microwave Near Field Reflection Property Analysis of Concrete for Material Content Determination  an NA
23 and rectangular waveguide were used to perform a near field and m icrowave NDE inspection technique to determine the cure state of concrete. Bois measured the amount of signal reflection produced from sample concrete slabs. The amount of reflection was used to characterize the concretes cure state. This is an importan t issue in the construction industry because being able to detect the cure state of concrete at a construction site can mitigate days lost in extraneous delays waiting on the concrete to cure. Additional structures can be placed on the concrete sooner lea ding to faster completion dates. Bois showed that creating this type of inspection system would not only be useful for the construction field but it can be achieved simply with an NA and an open Fi gure 2.5 Figure 2.5 : Bois Microwave Imaging System for Concrete Cure State Inspection In the Bois MIS the waveguide transferred the microwave radiation to the specimen and the same waveguide captured the amount of radiation that was reflected back to the port system. The NA can quantify the waveguide
24 signal V+ to the reflected signal V measured at the port of the NA. This ratio of V to V+ is called the S 11 scattering parameter or the reflection coefficient. The reflection co efficient tells how much power was transferred from the waveguide and how much was reflected back to the source from the concrete specimen. Bois shows that the magnitude of the reflection coefficient is directly proportional to the cure state of the concr ete. Bois shows that two factors affects concretes cure state, the water content ratio (w/c) and course aggregate to cement ratio (ca/c). As the concrete mixture of sand, cement and ca cures, the reflection coefficient magnitude measured by the NA increa ses because the amount of course aggregate increases. The microwave radiation injected into the sample by the waveguide scatters more when it is incident on the ca versus water or mortar. The more ca the more the field scatters the larger the amplitude o f the reflected field and the larger the magnitude of the reflection coefficient. In concrete the w/c and ca/c will not be the same everywhere. Concrete is very inhomogeneous and due to this fact, the reflection coefficient amplitude will ver y as you scan across the concrete specimen. Bois exploited this fact by conducting two experiments, hitting the specimen with 3GHz (S band) radiation and 10GHz (X band) radiation. Instead of performing a raster scan of the entire 8inx8inx8in slab of conc rete which is typical, Bois conducted 20 and 160 independent measurements on four sides of the concrete slab (excluding the top and bottom). At 10GHz the space and Bois sees that as the concrete cures the amount o f scatter from the increased ca/c increases but the measurements at this frequency do not provide information on the w/c. When the Bois MIS generates radiation at 3GHz the radiation wavelength is
25 measurements provide informa tion about the background material (everything except the ca). The background material consists of the cement paste which is an indication of the w/c. Like the first experiment Bois sees that the amplitude of the reflection coefficient increases as the c a/c increases and the w/c decreases indicating an increase in cure state for the concrete. waveguide and a NA. The only drawbacks of the experiments is that it does not mention the geometry of the rectangular open ended waveguide nor does it mention anything about the MIS system would definitely affect the amplitude of the reflection coefficient and the concrete cure state classification. Bois might not have thought it was necessary t o mention but since he did not mention it, it appears that impedance matching was not for being able to scan and cover larger amounts of sample surface area at a time but their geometries do not produce high resolution systems and should not be used for microwave imaging techniques requiring sub mm or less resolution. Muhammed S. Boybay presents analysis of a near field microwave imaging experiment that used an open ended coaxial probe to achieve higher imaging resolution than Bois. In the paper Open Ended Coaxial Line Probes with Negative Permittivity Materials  Boybay experiments with a coaxial open ended waveguide that was terminated with a two layered medium. The sample under test is sandwiched between a negative layer and is backed by a perfect electric conductor (PEC). Figure 2.6 shows a diagram of the coaxial waveguide terminated with the two layered medium.
26 Figure 2.6 : Open ended Coaxial Line Terminated by a Two Layered M edium Boybay experiments with the optimum target lift off distance (d) and the thickness and constitutive parameters of the negative med motivation stems from recent development in the area of double and single negative materials. Terminating coaxial probes with negative materials has revealed that the properties of near field probes can be improved cons iderably. Boybay sees that optimizing the negative layer thickness and probe to sample lift off distance gives rise to significant enhancement in probe sensitivity which allows the probe to have higher material and detection resolutions. The dielectr negative layer is assumed to be infinite in the x y plane and the permeability is assumed to be equal to free space permeability ( o ). For different negative layer thicknesses, the reflection coefficient was analyzed as a function of lif t off distance. Similar to Bois, Boybay optimizes the lift off distance and layer thickness by analyzing the reflection magnitude measurements. One thing that Boybay did that Bois did not do was he also measured the phase. The reflection magnitude and p hase were
27 negative layer thicknesses (see Figure 2.6 ). Figure 2.7 : Magnitude of the reflection coefficient vs. target distance (left); Phase of the reflection coefficient vs. target distance ( right) From the reflection magnitude plot displayed in Figure 2.7 Bombay deduced that the optimal probe to sample lift negative layer thickness was t = 0.8 mm and d = 0.87 mm. In addition, the phase of the reflection coefficient has the highest slope at the target distance at which the minimum reflection is observed. At these target distance and negative layer thickness combinations, the reflection coefficient is sensitive to the target location such that a small change in the target location causes the maximum change in the phase of the reflection coefficient. As a result the minimum reflection magnitude or the highest phase slope conditions correspond to the most sensitive probe configurations. Conventional coaxial lin negative layer. Boybay mentions that the optimum lift off distance is equal to zero for this case. Meaning, the closer the target is to the coaxial probe, the more sensitive the probe becomes and higher resolution is achieved. This is valuable information because the LEAP NFMWIS does not employ a
28 negative layer. The major take a off distance from target to probe matters. The lift off distance can affect the sensitivity or dyna mic range of the MIS reflection magnitude measurements (one of the most typical measurements made by MIS). It is best to experimentally optimize the target to probe lift off distance. Also, Bombay exploits the importance of measuring the reflection magni tude and aids in showing its effectiveness as a parameter for generating microwave have been a little clearer. The MIS setup that was used to make the reflection measure ments was never divulged. Yes he mentioned that the operation frequency was 5GHz but he never mentioned what device was used to make the reflection measurements. One would assume that it was a NA but you can also make reflection measurements with a time domain reflectometer (TDR). Bois or Boybay did not mention the power level of the measurements devices which directly correlates to the size of the reflection coefficient magnitude. None the less Bois and Bombay show that effective MIS can be construct ed with open ended waveguides. 2.4 .2 Near Field Scanning Tip Techniques NDE methods of imaging surface and subsurface structures and material properties are critical for acceptance testing and failure detection of countless applications including: sem iconductor defect detection, thin film resistivity measurement, continuity of embedded transmission lines in printed circuit boards (PCB), and substrate epoxy void detection. These applications require high detection resolution in order to image their ma terial properties. Imaging with open ended rectangular or coaxial cables are adequate microwave imaging techniques, as demonstrated by Bois  and Boybay  but they are
29 better for applications that require large scan surface areas and lower resolution. In order to image defects in semiconductors or the embedded traces of a PCB, a MIS must be built to achieve m resolution. G.E. Ponchak  a nd Yaqiang Wang  agree that ultra high resolution MIS can be achieved with scanning tip waveguides. Scanning tips are commonly used for near field imaging applications because they make great use of the evanescent wave interaction between the sample under test and the scanning tip. Wang explains how using an open ended coaxial cables with a protrud ing center tapered tip has the advantage of supporting microwave signals with nearly no cut off limit and producing highly confined electromagnetic fields. The confined field confines the measurement area on a sample under test increasing the resolution o f the topographic dielectric measurements. Wang and Ponchak show that there are three important elements to the fabrication of scanning tip waveguides: length, diameter and scanning height or lift off distance. Boybay mentioned that without using matchin g layers to match and couple the microwave energy from the waveguide to the sample under test, probe measurement sensitivity increases as the scanning height decreases. According to Boybay the ideal scanning height for scanning tip probes would be when th e tip is in contact with the sample. Wang and Ponchak agree with Boybay that measurement sensitivity increases as the scanning height decreases but explain that with coaxial tips, parasitic capacitive coupling with the sample material is a problem that li mits coaxial tip microwave imaging techniques. Parasitic capacitance or stray capacitance can cause unwanted signal oscillations skewing the dielectric measurements made by a MIS setup. Wang and Ponchack show two distinct design techniques to mitigate pa rasitic capacitive coupling.
30 In scanning tip near field applications, a microwave signal is sent through a coaxial cable and radiates as microwave energy through a EMF. Ponchak explains that due to the close proximity of the scanning probe required b y near filed techniques, t he EMF extend s outward from the probe tip a short distance. Ponchak explains that an object that is brought in close proximity to the tip will interact with the evanescent fields and change the loading of the scanning tip. Figur e 2.8 shows a schematic of the scanning tip and the equivalent circuit produce between the tip and sample under test. Figure 2.8 : Schematic of Microstrip R esonator Used in a Microwave Evanescent P robe Ponchak states that the dielectric properties of a sample generates unwanted increases in the probe capacitance which results in a lower probe r esonant frequency Ponchak validated this notion by detecting these variations in the probe resonant frequency, fo, by measuring the reflection coefficient magni tude, |S 11 |, of the scanning tip resonator with a NA.
31 Figure 2.9 : Measured |S 11 | of channelized coplanar waveguide probe both with and without a high resistivity silicon sample near the probe tip Figure 2. 9 shows the plot of the measured reflectivity when the probe is in isolation (no sample underneath) and it shows the reflection magnitude measured when a silicon sample was place under the scanning tip. Notice how the resonant frequency of the scanning tip is shifted from approx. 9.86GHz with no samp le placed close to the tip to approx. 9.82GHz when the silicon sample was placed in close proximity of the scanning tip. Ponchak employs a microstrip scanning tip design in order to reduce the unwanted parasitic capacitive coupling. Two microstrip de signs were fabricated. The fir st uses a channelized coplanar waveguide ( CCPW ) fabricated on a 0.3x175 cm (3000x1750000 m) r = 2.2) substrate. The center conductor width is 0.127 cm (1270 m) and the slot width is 0.028 cm (280 m) exc ept at the probe tip which is tapered to a point. Copper foil is used to connect the upper and lower ground planes to form the CCPW. Shorting the ground planes eliminates unwanted transverse electromagnetic modes (TEM) that result from the electromagneti c wave reflecting off of the boundaries between the conductor and dielectric insulation in coaxial waveguides. The second probe is a stripline
32 scanning tip fabricated on r = 3.8 R T/ Duroid with a strip width of 0.5 cm. The substrate provides a dielectric for the scanning strip/tip. This provides noise immunity and reduces the effect of the parasitic capacitive coupling caused by close sample proximity. Additionally, bo th probes have a 20 m diameter wire tip at the en d and are mounted in an aluminum fixture to eliminate coupling to other probes and decrease the background noise that would decrease the probe sensitivity. Wang introduce s a new microfabrication method to make silicon coaxial tips Similar to Ponchak Wang employs a scanning tip with a microstrip design. The coaxial silicon tip microfabrication process is outlined in Fig. 1. Figure 2.10 : Wang Coaxial Tip Fabrication P rocess First, a 1 thick thermal oxide is grown on a conductive Si wafer with a resistivity of 0.005 cm [see Figure 2.10 (a) ] Then an oxide disk is patterned by standard photolithography and buffered hydrofluoric (BHF) acid etch ing [see Figure 2.10 (b) ] The
33 exposed Si is etched by a reactive ion etching (RIE) t o form a tip precursor [see Figure 2. 10 (c) ]. Next, a deep reactive ion etching (DRIE) process is performed using an STS Mul tiplex ICP system to form a tip shaft that determines the length of the tip. The oxide disc is removed by hydrofluoric (HF) acid after the tip is sharpened using the oxidation sharpening method [ see Figure 2. 10 (d) ] To form the inner conductor of the coaxial tip structure [see Figure 2. 10 (e) ], 300 nm thick Cr film is then deposited on the wafer. This film is patterned by photolithography using negative tone SU 8 resist to cover the tip feature. The insulation layer of the coaxial tip is a 1 thick SiN layer deposited by plasma enhanced chemical va por deposition (PECVD) [see Figure 2. 10 (f) ]. The outer shield metal layer is a 1 thick Cr film deposited by sputtering. It is patterned by another SU 8 photolithography and etched to f orm the shield pattern [see Figure 2. 10 (g) ]. The key step in fabricating the coaxial tip is the ti p exposure process. The fina l coaxial tip structure is formed by a Cr wet etch and a SiN RIE. Figure 2.11 is an micrograph of a coaxial tip with an opening to the silicon tip process shown in of Figure 2.10 Figu re 2.11 : Micrograph of a Wang Fabricated Silicon Tip The Wang and Pochnak experiments are good to compare because they used scanning tip waveguides in two distinct MIS designs. Pochnak uses a scanning tip coaxial cable
34 fed by a 0.5 12.5 GHz signal gener ator t hrough a circulator. A crystal microwave detector connected to the the circulator produces a DC voltage proportional to the magnitude of the reflected signal. Samples are mounted on the xy stage platform and are vibrated in the z direction by a sole noid at a rate of a pproximately 100 Hz. This enables synchronous detection of the signal by a lock in amplifier to increase the SNR. A LabVIEW program controls the positioning of the sample in three directions as well as measuring and monitoring the outpu t voltage of the lock in amplifier at each position of the sample. Near field scanning tips are characterized by determining the spatial resolution and minimum detectable sample size The spatial resolution is typically determined by imaging a small samp le and rep orting the full width of the image at one half of the maximum signal level  To characterize the resolution of the CCPW, a 20 m diameter wire resting on a glass plate is imaged along the x and y axis The scanned image of the wire along the x and y axis for the CCPW at 10GHz is shown in Figure 2.1 2 Figure 2.12 : Scan of a 20 pm diameter wire along the x and y axis using CCPW evanescent microwave probe
35 The CCPW was able to resolve the PCB trace 25.5 m and 22 m along the x and y axis mm resolution. Figure 2.13 : Circuit Schematic for SNMM S can Wang generated a s canning near field microwave microscopy (SNMM) system out of two frequency sweepers, 2.36 GHz and 2.36 GHz + 93 KHz and two mixers. The t wo mixers were used to modulate or down convert 2.36 GHz to 90 KHz, which lies in the working frequency range of a lock in amplifier. A directional coupler was use d to guide the microwave source from sweeper A to the coaxial tip, and it coupled the reflected signal from the coaxial tip to one mixer. An atomic force microscope (AFM) probe was used as the sample under test for his experiment. The output of this mixe r contained the information from the sample ( AFM probe ) The other mixer mixed direct signals from sweepers A and B the resulting signal was fed as the reference signal for the lock in amplifier. The lock in amplifier output was delivered to the data acquisition (DAQ) channel of the AFM system to build up the microwave image of the sample. The SNMM system that Wang developed was able to image the dimensions of the AFM probe.
36 Figure 2.1 4 : Simultaneous SN MM images by the coaxial tip. (lef t ) AFM internal sensor image; (right ) Microwave amplitude image The cantilever width is correctly resolved in both AFM and microwave images, around the Figure 2.1 4 SNMM system achieved sub why these two experiments on scanning tip microwave imaging techni ques are good references for designing and implementing scanning tip probes into a MIS. Both scientists designed micostrip scanning tip probes in order to reduce measurement noise caused by RF interference and parasitic capacitive coupling due to the clos e proximity of a test sample. Each scientist fabricated scanning tips with m geometries in order to achieve sub mm resolution. The differences come in the form of the MIS used to capture MIS used lock in amplifiers ( phase sensitive detector used a crystal microwave detector connected to the port of a circula tor in order to measure and image the DC voltage that was proportional to the magnitud e of the reflected signal. lock in amplifier with an NA.
37 Lock in amplifiers are useful for phase sensitive detection techniques (PSD). The amplifier locks onto signals with the sam e frequency as the reference signal input. The voltage output of the amplifier is directly proportional to the phase difference between useful inspection techniqu e for microwave imaging systems. Bois  Boybay  Wang  Ponch ak  all produced microwave imaging systems that generated topographic images based on measuring the dielectric variances in inhomogeneous materials. Each microwave imaging applications previously mentioned imaged samples with relatively high dielectric const ants. As the demand for the medical application for microwave imaging increases, imaging objects with lower dielectric constants such as laminates, fluids, and tissue are more desirable. For lower dielectric constant materials sensitivity and imaging res olution has seen success in measuring not only the magnitude of the reflection coefficient but also the phase difference between the MIS transmitted signal (Tx) and the measured received signal (Rx). 2.4 .3 Near field Phase Detection Approach Xiao Fe ng Zhu presents a near field imaging phase detection approach in the paper Near field Microwave Phase Imaging by a Spintronic Sensor  Zhu agrees that a MIS c properties but iterates that this can only be done with high accuracy when both the amplitude and the phase of the radiated and reflected microwave fields are measured. Zhu experiments with dielectric imaging by measuring the amplitude and phase differe nce of the Tx and Rx
38 sensor scan and shows the sensors capability to achieve both phase and amplitude resolution on a sub wavelength scale. Figure 2.15 : Zhu Spintronic Phase Detection MIS Figure 2.15 shows the PSD experimental setup. The microwave power is split into two coherently microwave signals that travel two independent paths. In one path the microwave signal is directly injected into the sensor from a coaxial cable while in the other path the microwaves radiate on the sensor from a horn antenna. The direct mi crowave signal driven by the source and the radiated energy from the horn antenna are coupled at a spintronic sensor. The sensor mixes the source signal and the voltage signal induced in the spintronic sensor resulting in a homodyne DC voltage that contai ns information on the phase difference between the two microwave signals. A lock in pre amplifier is used to measure the microwave induced DC voltage signal from the sensor. Using the system pictured in Figure 2.15 phase difference measurements we re taken for two different liquids: water and isopropyl alcohol. The liquids were placed in channels of a dielectric grating microwave the depth were fabricated with a milling machine. The sensor was field response of the microwave field that transmits through the grating (Rx). Near field phase diff erence
39 measurements were captured for the entire grating and imaged by performing a 2D raster scan over the surface area of the PMMA grating using a 8.1GHz (37 mm wavelength) own in Figure 2.16 Figure 2.16 : Near field Phase Difference Measurements of Liquid Filled PMMA Grating: (a) Grating filled with isopropyl alcohol; (b) Grating filled with water Figure 2.16 (a) shows that the spintronic sensor, situated grating detects a periodical modulation in both the amplitude and the phase of near field microwave fields when scanning the grating filled with isopropyl alcohol. The dielectric constants were increased by filling the channels with water, the EMF shifts and a much stronger contrast in the 2D phase difference image appears as shown in Figure 2.16 (b) The pronounced phase shift due to water significantly enhances the contrast of sub wavelength features of the grating, which reveals the power using a spint ronic sensor to detect amplitude and phase difference measurements in liquids with lower dielectric constants compared to metal samples. Figure 2.16 (a) and (b) shows that near field phase detection systems can achieve sub mm resolution. Compared with traditional transmission li ne based approaches presented by Wang  Boybay  and Ponchak  where the parasitic noise from the tran smission lines in
40 close proximity of test samples limit the sensitivity of the technique Zhu claims transmission line techniques only provide a qualitative contrast but spintronic sensors can deduce a quantitative value of the dielectric constant. Physically, both the induced sensor voltage and the phase difference between the microwave source are related to the transmission of microwaves in the media and are a representation of the material dielectric properties. Therefore, the dielectric constant can be precisely determined from the microwave propagation in comparison with the case The ability of between the PMMA and liquids not only shows the superior sensitivity of ph ase detection systems over conventional qualitative measurements made by Bois, Boybay, Wang and Ponchnak but it also puts phase detection approaches at the forefront for microwave medical imaging applications. 2.5 NEAR FIELD MEDICAL IMAGING APPLICATIONS The interaction of electromagnetic waves and matter depends on dielectric properties which can be directly related to various types of biological elements due to their variable degree of water content: bone, fat, muscle, etc. This specificity can be exploited in different tissue types and even offers the rationale for detecting tumors. P ermittivity of biological tissues is normally high at lower frequencies due to the insulating effect of cell membranes, and decreases over higher frequencies due to d ispersion. Fat, bone and lungs are examples of low water content tissues and muscles, internal organs, blood and tumors are examples of tissues with high water content. Among possible applications, breast cancer imaging remains a high research priority b ecause of its high occurrence. Roughly 200,000 new cases of breast cancer are
41 typically diagnosed in the U.S. every year, with an estimated 25% 30% of women dying from the disease making it the second largest cause of female cancer deaths in the U.S  The microwave imaging techniques presently being pursued are generally either radar or topography     Grzegorczyk presents a 3D imaging system for generating 3 D tomographic images of breasts in his paper entitled Fast 3 D Tomographic Microwave Imaging for Breast Cancer Detection  It is not uncommon to wait tens of hours or even days for a single 3 D microwave tomographic image. Most microwave breast cancer detection systems are burdened with significant computational times limiting their clinical utility. Grzegorczyk overcame th ese limitations by presenting a MIS which achieves an exam time of less than 2 minutes and produces 3 D tomographic images in minutes as well. The 3 D tomographic MIS (see Figure 2.17 ) employs an array of 16 monopole antennas is organized in a circu lar fashion. Each antenna sequentially transmits an electromagnetic wave which propagates through the breast within the imaging region. Measurements are collected at the remaining 15 antennas so that those close to the radiator mainly measure waves refle cted off tissue surfaces whereas those opposite to the transmitter mainly measure transmitted waves. The multi view scattered intensity and phase distributions provide information about the local dielectric properties of the tissues. This information is t ranslated into tissue permittivity and imaged.
42 Figure 2.17 : 3 D Tomographic MIS Examination Chamber: (left) 3 D representation of the tomographic chamber; (right) Assembled antenna hardware In order to limit reflections off the tank boundaries, a lossy liquid comprised of a mixture of Glycerin and water provides a biologically sterile medium into which the down to 140 dBm. Such a low noise floor requires an antenna to antenna isolation of 150 dB. Grzegorczyk validates the detection specificity the 3 D tomographic MIS by comparing breast images of a patient taken with an MR and then comparing it to 3 D tomographic images generated by the DAQ platform. The high reso lution MR image shows a malignant tumor detected in the left breast shown in Figure 2.1 8 In order to achieve high resolution the 3 D tomographic system collects a high number of samples and achieves low examination times by achieving a high sample rate. The system collects 240 data points (16 transmitters with 15 receivers) in roughly 1 s. In order to achieve sub centimeter resolution, an operating frequency depend on the breast composition. A mastectomy was performed on the patient in order to remove the left breast. The breast was then imaged by the 3 D
43 tomographic breast imaging system. Figure 2.19 shows the 3 D tomographic image generated when the breast was examined by the 3 D tomographic MIS. Figure 2.18 : MR Imaged tumor in right breast: (left) Tumor Axial View (right) Tumor axial View After Baseline Subtraction Figure 2.19 : 3 D Permittivity Breast Image from 3 D Tomographic MIS Figure 2.18 shows a directional averaging of the 3 D permittivity images for each breast compressed into a single coronal image (similar in concept to that observed in standard mammograms). Consistent with the previous images and discussions,
44 the tumor is readily vi sible in the right breast image as an elevated permittivity zone, with no discerning features visible for the right breast. D tomographic breast MIS images the reflection magnitude and phase produced in 16 monopole antennas to an EMF scatt ed from breast tissue. His experiments demonstrate a phase detection approach that enables a MIS to quantitatively  of being able to use phase measurements to image lower dielectric materials such as liquids, tissue and fat. 2.6 SUMMARY Near field microwave imaging is concerned with quantitative measurement of the microwave electrodynamic response of materials on length scales far shorter than the free space wavelength of the radiation  Near field microwave imaging techniques are very used for NDE inspection techniques. Microwave imaging is performed with a MIS that can be used to measure material dielectric properties in materials (permittivity, conductivity, reflectivity) with higher dielectric constants such as concrete   and traces embedded in a PCB [Pochnak .pdf] MIS generally consists of a microwave source, waveguide, receiver, data acquisition (DAQ) device, and a computer to process the data and co nstruct a visual dielectric topographic image of an object under study. Figure 2.20 is an example of a gener ic MIS. MIS measurements can be acquire data via a 1 port network     or two port network configuration 
45 Figure 2.20 : Generic Microwave Imaging System  Three distinct near field microwave imaging techniques were discussed in this chapter: open ended waveguides, scanning tip probes and phase sensitive detection techniques. Open ended rectangular or coaxial waveguide techniques should be used for ima ging applications requiring cm resolution  but scanning tip waveguides should be employed in applications that require sub mm resolution   In order to produce MIS with m resolution it is suggested that the phase of the reflected signal induced in the waveguide be measured and compared to the transmitted signal from the MIS microwave source   T his chapter also suggests that resolution directly relates to the geometry of the waveguide and the scanning lift off distance    Reviewing these three near field imaging techniques was useful because it provides background on the motivations behind the LEAP NFMWIS development efforts described in detail in chapter 3 Early developments of the LEAP NFMWIS used an open ended rec tangular waveguide and a 1 port MIS. The reflected signals from the test sample were rectified by a crystal detector and used a DMM to capture the root mean square (RMS) of the rectified signal from the crystal detector output. This configuration
46 of the LEAP NFMWIS is properly coined Generation I because it was the first system developed. Generation I of the LEAP NFMWIS generated microwave images using software running on a PC to move an x y positioner. The same software would acquire the DMM voltage me asurements at that x y position and would render a 2D Plot of the reflected voltage amplitudes when x y scanning commenced. The desire of the LEAP NFMWIS is to achieve sub mm resolution and this was not achieved with Generation I. Leveraging the ideas of Wang  and Po nch ak  a scanning tip waveguide technique to spatially confine the EMF radiation to more discrete points on the sample under test was the motivation behind LEAP NFMWIS Generation II. Generation II used a fabricated coaxial scanning tip waveguide employed with the same MIS setup as Generation I. An increase in resolution was witnessed in the images acquired between Generation I and II systems but Generation II images appeared to h ave noise attribute to the parasitic capacitance caused by the close proximity of the sample under test and lossy connections   Generation III of the LEAP NFMWIS employed a scanning tip wav eguide with a tapered tip following the experiments by Pochnak et. al Noise was reduced by terminating the scanning tip to a coaxial cable using SMA connectors. Also additional shielding was added to reduce the parasitic capacitance coupling caused by the sample under test and isolated the scanning tip from unwanted RF noise in the testing environment. A vast improvement in image contrast and resolution has been witnessed with Generation III.
47 CHAPT ER III OPTIMIZATION OF THE LEAP NFMW IMAGING SYSTE M 3 1 MICROWAVE IMAGING PROBLEMS 3.1 .1 Constructing Valid Numerical Models FDTD enables computational modeling and simulation of electromagnetic phenomena. This is useful when there is a need to understand how EMF behaves in or on an object (e.g. cell phone antenna, cable dish, transformer coils, etc.). This allows engineers and scientists to gain information about a component or entire system before fabrication but constructing a valid numerical model has several challenges, 1. Accurately defining com putational domain or object structure for the simulation. 2. Accurately defining computational boundary conditions 3. Accurately defining material properties (e.g. permittivity, permeability and conductivity). 4. Modeling the field distribution in the near field (kz < 1, where z = source to  3.1 .2 Real time NFMW Imaging Small signals are typically used in NFMWI techniques and usually amplification is required to bring these signals into the measurement range of the data acquisition device. attribute more noise to a MIS because the amplifier is going to amplify the noise as well as the desired signal embedded in it. Noise issues also arise in the waveguide and with the measurement setup.
48 3.1 .2.1 Systematic errors Several systematic errors can arise when using an MIS to generate the reflection topography of an object unde r test. 1. Impedance mismatches between MIS components 2. Flexing of the RF cables  3. Positioning between the sample and waveguide/aperture (in accurate measurement alignment)  3. 1 .2.2 Drift Errors Long measurement times can cause drift errors in measurements due to various wave propagations in the RF cables used in the MIS. Measurement drift can also occur due to temperature increases of the internal hardware of the data acquisition device  The a dvantages of MIS: a bility to focus the energy r elatively low cost emits n on ionizing radiation The limitations of MIS: hard to achieve high s patial resolution low p enetration depth susceptible to EMF interference 3.1 .3 Open Ended Waveguide (Scanning Tip) In the case of the NFMWIS a scanning tip was designed to achieve sub mm resolution in the near field, D Tip TIP is the aperture diameter or tip curvature)  A probe tip of this size interacts with the device under test (DUT) in a spatially confined region. The near field of an aperture carries with it spatial information on the order of the aperture size  The spatially confined field distribution near the aperture is effectively a sampling function of small spatial extent. The tip is placed close to the sample in order to take advantage of this sampling affect  The aperture of the scanning tip and its physical performance in the MIS is unknown and should be solidified
49 experimentally. Essentially the scanning tip is an open ended waveguide that serves as an antenna radiating and receiving (1 port networks) micr owave energy. Since microwave imaging techniques typically use small signals, adequate shielding of the scanning tip is essential for high SNR. 3.1 .4 Phase Extraction Challenges Near field microwave imaging measurement techniques typically suffer from small signal to noise ratios (SNR). Techniques require instrumentation that provides signal enhancement, noise reduction, and long term stability  An arbitrarily high SNR can be achieved while still preserving long t erm stability by implementing phase sensitive detection (PSD) mechanisms  PSD is often implemented with an RF mixer in conjunction with a low pass filter. Mixers are three port networks (2 inputs, 1 output) that multiply a local oscillator signal (LO) with an RF signal and the output of the product is produced at the IF out put. A DC signal arises when two sinusoidal signals are mixed together and the low pass filter isolates this signal so a device can measure it. The amplitude of the DC signal is proportional to the phase difference between the two mixed signals  Problems arise with the PSD approach due to the performance isolation of the mixer. RF mixers allow signals from the input ports (LO and RF) to leak to the output port (IF) contaminating the signal and in turn contaminating th e DC signal and phase measurement. Mathematically, the frequency components generated by a mixer are, (3.1) n and m: are integers We only want the fundamental output frequency (when n=1 and m=1), the existence of
50 all other harmonic terms creates significant problems  Elimination of these distortion products is critical in developing an efficient PSD approach. 3. 2 LEAP NFMWI SYSTEM OVERVIEW Following suite of microwave imaging setups, generically the NFMWIS was composed of an X Y pos itioner to perform raster sc ans; a waveguide to transmit the microwave radi ation to the sample under test; a response to the returned microwave radiation and a PC to collect the data, perform post process ing and to generate the image. In the LEA P NFMWIS t he waveguide that transmits the microwave radiation also measures the response of the waveguide to the received near field radiation making the NFMWIS a 1 port network. Figures 3.1 and 3.2 show the current LEAP NFMWIS setup. This thesis cover s two main approaches that were developed during the optimization of the LEAP NFMWIS setup. Approach 1 measures the amplitude of an induced voltage signal produced in a coaxial tip antenna (CTA) to the reflected microwave energy from a test sample placed a small lift off distance away in the near field. Approach 2 measures the phase difference between the received AC signal induced in the CTA versus the transmitted signal generated by the microwave source.
51 3. 2 .1 Approach 1 LEAP NFMWIS Voltage Amplitude Detection System Figure 3.1 : Block Diagram of LEAP NFMWI Voltage Amplitude Detection System 3.2 .2 Approach 2 LEAP NFMWI Phase Detection System Figure 3.2 : Block Diagram of LEAP NFMWI Phase Detection System Directional Coupler Waveguide: Rectangular/Coaxial Scanning Tip/ Copper Tapered Tip Tx Rx
52 3.3 LEAP NFMWIS OPTIMIZATION METHODS As mentioned before microwaves are good at detecting changes in the dielectric properties of materials and due to their high dielectric constants, metals are some of the best candidate s for microwave imaging applications In order to monitor the image quality of each LEAP NFMWIS approach. A calibration sample was fabricated from aluminum to help gauge the optimization process of each LEAP NFMWIS approach (calibration sample is shown in Figure 3 .3 ). Figure 3.3: Sample (dimensions are in inches) Assembling an efficient MWIS a steady deve lopment process is required in order to optimize all the elements of the imaging system Chapter 3 discusses the LEAP NFMWIS optimization efforts that were made in the areas of system synchronization, scan execution time, ant enna design, imaging resolution enhancement and image post processing.
53 3.3 .1 System Synchronization When creating accurate dielectric topographies of materials there must be synchronization between the position and the measurement. To generate the best microwave images, the PC, X Y positioner (scanner) and DAQ device need to be synchronized. Initial development efforts of t he LEAP NFMWIS utilized two computers to execute a scan. One computer controlled the scann er and the other computer ran a MATLAB script in order to read measurements from the DAQ device. Each computer ran as two independent state machines. Scans could still be achieved and the system generated images but when trying to achieve a MWIS with sub mm resolution, measurements need to line up with the current scan location on the sample. The LEAP NFMWIS scanner is a n Arrick Robotics MD2 scanner, which a ccepts serial commands through an RS 232 bus. A single MATLAB script ( scan.m in appendix) was developed to issue move commands to the scanner and also coordinated the retrieval of measurements from the DAQ device. This script ran on one PC alleviating t he dependency of two computers. A Flow Diagram of the imaging algorithm is shown in Figure 3 .4 One can see that the scanner is moved to a desired position, waits, DAQ occurs and the scanner is moved to the next position, waits, and DAQ occurs and repeat DAQ always occurs between positioner movements showing that the synchronization of NFMWIS components was achieved. 3 .3 .2 Scan Execution Time An imaging system is neither effective nor efficient if it takes a day to produce a microwave image. The benefit of the MATLAB algorithm implemented in Figure 3.4 is that it synchronize s the positioner movements and DAQ, w hich yields improved
54 micr owave images because there is better identification of sampl e defect locations The downside is that it took a long time to scan test samples. This was unacceptable to the LEAP Team because without synchronization we were performing scans much faster. This is an example of where system optimization efforts are required. The algorithm provides synchronization between sample position and DAQ allowing for better resolved images that is a must, but the algorithm leads to long scan times which shows inefficiency in the system design At this stage one must begin to consider all elements of the MWIS that are involved in the image acquisition process. The two physical components that could affect scan times are the positioner and the DAQ s. The positioner of the LEAP NFMWIS is an Arrick Robotics MD2 scanner with two stepper motors to independently control the 2 axis X Y movements. The DAQ device has alternated between a DMM and spectrum analyzer. During initial development the positioner motors were ran at very slow speeds. After reviewing the Arrick Robotics MD2 user manual it was seen that increased motor speed s could be achieved by prescribing the scanner with a velocity profile Since the two scanner motors are independent distinct profiles can be issued for each motor. The velocity profile is provided with a sequence of three numbers separated by semicolons [ start velocity of # steps/ s : max velocity of steps/s : acceleration/deceleration slope of # steps/s 2 ]. In order to the positioner drive stepper motors four signals are sent in specific sequence in order to control clockwise (CW) or counterclockwise (CCW) movements If t he frequencies of the control signals are too high, the motor s fail to interpret the m and motion will not occur. Thus, the velocity profile s of the positioner motors were
55 experimentally optimized in order to attain the maximum motor velocity that achieves motion without causing excessive sample vibration The optimized velocity profile m oves positioner motors 1 a nd 2 at a start velocity of 400 steps/s and achieve s a maximum velocity of 6 00 steps/s with an acceleration/deceleration of 2 00 steps/s 2 From he serial bit rate (baud rate) of the positioner controller can be set to a maximum of 115200 bits/sec. It was thought that issuing commands at the considerably. I nitial development efforts showed th at the bit rate had marginal effect on o verall scanning execution times so it remains at the default rate of 9600 bits/sec. Through experimentation it was easy to see that the long execution times of the LEAP NFMWIS scanning algorithm sho wn in Figure 3 .4 are attributed to two major areas: 1. Object r aster s can ning algorithm 2. Embedded delays within the DAQ a lgorithm For the algorithm in Figure 3.4 t he X position of the scanner corresponds to the sample length ( X length ) and the Y position corresponds to the sample width ( Y width ). First, the scanning algorithm breaks up the designated sample scanning area into rows (x steps) and columns (y steps). T he scanner is instructed to t raverse the width of the sample by moving the design ated stepper motor a specified number of y steps and record the DAQ device measurement into a 2D matrix Once the scann er has traveled the entire sample width it will stop DAQ and reverse the y number of steps to return back to top of the sample, move ove r a specified sample length ( number of x steps ) and began the DAQ
56 process again by traversing the sample width and recording measurements. Note, measurements are only recorded when the width of the sample is being scanned. Figure 3.4 : Flow Diagram of Initial LEAP NFMWIS Raster Scan Algorithm Let the travel time it takes for the position er to return to the top of the sample be denoted by Y REPOSITION For the algorithm implemented Figure 3.4 Y REPOSITION 6 seconds (see scan.m script in Appendix). The travel time multiplied by the total number of x and y steps (X STEPS and Y STEPS ) it takes to traverse the designated sample scanning region contributes a considerable amount of time to the overall scanning execution time S ample reposition occurs before incrementing an x step, so the total time it takes to reposition the sample is,
57 (3.2 ) For example, if the scanning area of a sample is designated as 100 X STEPS and 100 Y STEPS according to equation 3.2 the algorithm wastes 6 seconds x 100 X STEPS = 6 00 seconds ( 10 minutes) in sample repositioning time This latency was removed by removing the instruction to reposition the sample before incrementing the sample length. The scanning algorithm in Figure 3 .4 was improved by taking measurements as the width of the scanning region is traversed (top to bottom), as before but o nce the end of the sample is reached, instead of stopping DAQ and traveling back to the top of the sample before reconvening scanning the DAQ is stopped and the scanner moves a specified sample length (x steps) DAQ begins and the width of the scanning region is traversed (bottom to top). Like the initial algorithm all data is stored in a 2D array with dimensions X STEPS by Y STEPS The improved raster scan algorithm is shown in Figure 3.5 and the MATLAB code is located Appendix B This algorithm required data post processing in order to yi eld the appropriate images. Every even column of the 2D data matrix had to be flipped from bottom to top in order to produce an appropriate microwave image (see scan.m in Appendix A). Eliminating the extraneous sample repositioning movements automatically improved the execution time of the LEAP NFMWIS by 10 minutes.
58 Figure 3.5 : Flow Diagram of Optimized LEAP NFMWIS Raster Scan Algorithm The algorithm implemented in Figure 3 .5 has a sampling rate of approximately 1 square inch per 20 seconds (1 sq. inch/ 20 seconds) Th e delays between movements were optimized in order to limit measurement error due to sample vibration. Initially one wou ld think that the larger delays to allow for the sample to stop the better. Yes larger delays lead to minimal vibration and reduced measurement error but it also leads to longer scanning execution times. Example, consider the DAQ algorithm of the M ATLAB script shown in Figure 3.4 The course movement of positioner stepper motors causes the sample to move relative to
59 the positioner movements. Fast scanning execution time is desirable but it comes at with a cost. Not allowing sufficient delays lead s to measurement error. It causes the sample under test to wiggle or vibrate due the momentum the sample builds due to the quick movements of the positioner. Placing a small delay between the positioner movements can mitigate vibrations of the sample. Ea rly revisions of the NFMWIS scanning software the major delays are: a 0.7 second delay between each y step (Y DELAY ) a 0.1 second delay between each lateral x step (X DELAY ) and Y REPOSITION time to re orient the sample before executing each x step These are just th e major delays in the algorithm and do not include the time it takes to execute the MATLAB scanner position code, the DAQ control code or the data processing code. All major delays in the DAQ process need to be optimized in order to maximize t he trade off between fastest execution time and minimal sample vibration. First, an approximation was performed to determine how much each delay contributes to the total execution time of the physical scanning process (Tot EXC ) Example, c onsider a test sample scanning region is defined as 100 X STEPS by 200 Y STEPS The total execution time of th e initial scanning algorithm is approximately, (3.3 ) X STEPS and Y STEPS : total number of x and y steps for a scanning region X DELAY = 0.1 sec and Y DELAY = 0.7 sec : delay between each x and y step Y REPOSITION = 6 sec : time delay to reorient sample before taking another x step Using equation 3.2 and 3.3 the approximate total sca nning execution time is 750 seconds (12.5 minutes) for a sample scanning region of 100 X STEPS by 200 Y STEPS
60 The impact each major delay has on the total scanning execution time can be approximated as, (3.4 a ) (3.4 b) (3.4 c) X D.I. and Y D.I. : impact of x step delays on overall scanning execution time Yrep D.I. : impact of sample repositioning delays on overall scanning execution time Using equations 3.3 and 3.4( a) 3.4 (b), for a test sample scanning region of 100 X STEPS by 200 Y STEPS the impact of the each major delay on the total scanning execution time is: X D.I. = 1.3%, Y D.I. = 18.7% and Yrep D.I. = 80.0%. The r eposition delays were eliminated with the raster scan algorithm developed in Figure 3.5. This algorithm reduces the total execution time by a minimum of 10 minutes and but most importantly increases system performance by 80%. Now consider the total scann ing execution time attributed by the x and y step delays For a 100 X STEPS by 200 Y STEPS scanning region, t he x step delays have a minimal 1.3% impact on the overall scanning execution time but the y step delay impact of 18.7% is considerable S ince data measurements are only extrapolated during Y position moves the y step delay mitigates system measurement error attributed to sample vibration The s hort er the delays between y step positioner movements, greater are the sample vibrations High sample vi bration l eads to poor microwave images and l onger delays between positioner movements leads to longer execution times. This is another point in the LEAP NFMWIS system where optimization had to be performed. Through experimentation the 0.7 second delay
61 be tween each y step scan po int was reduced to Y DELAY = 0.15 seconds, this reduces the overall scanning execution time for a 100 x 200 point sample scanning region to Tot EXC = (0.1 x 100) + (0.15 x 200) = 40 seconds (0.7 minutes). Optimizing the Y DELAY to 0.15 seconds improve d the system performance by 26.7% ( X D.I. = 40/150). 3 .3 .3 Antenna (Waveguide) Design transmits the microwave energy to the sample under test. The wav eguide of the LEAP NFMWIS has undergone three major design changes during its development. Initial development (Generation I) used an open ended rectang ular waveguide as the transmi ssion medium. The second (Generation II) used a coaxial tip antenna (CTA) made from an RG58U coaxial cable. The the third (Generation III) and most current setup, is evaluating the using of a tapered scanning tip was fabricated and was terminated to the end of the coaxial waveguide through an SMA connector. 3.3 .3.1 LEAP NFMWI S Generation I The first LEAP NFMWIS used an open ended rectangular waveguide to transmit microwave energy to the test sample. Rectangular waveguides are good for scanning samples with a large surface area such as concrete slabs   hig h resolution is not critical. Using the control sample a microwave image a microwave image was generated with the LEAP NFMWIS Gen. I. This image is pictured in Figure 3.6
62 Figure 3.6 : LEAP NFMWIS Open Ended WG Scan: (left) Calibration sample; (right) Microwave Image Initially the phase detection approach involved using the HP8559A spectrum analyzer (SA) Since the phase development approach was developed in parallel with the LEAP NFMWIS questions of using various DAQ hardware was posed. During the early development effort, a feasibility test was conducted with the Generation I system to see
63 what kind o f images would be produced when using the SA as the DAQ device instead of the Agilent DMM. Figure 3 .7 shows the imaging results of the SA feasibility test. Figure 3.7 : LEAP NFMWIS Open Ended WG Scan with SA DAQ Device 3 .3. 3.2 Generation II In near field applications requiring sub scanning tip waveguide. The diameter of the tip should be much less than the radiation wavelength  and the lengt h of the scanning tip should be equivalent to half the radiation wavelength in order to transmit all the energy from the source to the test sample. (3.5) (3.6) The second progression of the LEAP NFMWIS used a coaxial scanning tip antenna (CTA) that was made by removing the dielectric jacket at the end of a RG58U coaxial cable exposing the copper conductor. This design has the advantages of lower cost and easy of fabrication. The diameter of the inner conductor of the co axial cable is around
64 1.02 mm, which is much smaller than any other kind of probes LEAP had developed to this point. The length of the antenna was designed to be 1/2 wavelength (1.5 cm) to have the maximum return loss for the antenna. Figure 3 .8(a) shows the CTA tip implemented in the LEAP NFMWIS and Figure 2.9(b) shows some of the LEAP NFMWIS key components. Figure 3.8 : LEAP NFMWIS CTA waveguide: (left ) waveguide tip and (right ) signa l spliter and amplification (annotate picture) Figure 3.9(a ) shows an image generated with the mm CTA and Figure 3 .9(b) shows the image attained with the cm scale open ended rectangular WG. Better resolution of the calibration sample defects was achieved using the mm CTA as opposed to the cm scale open ended rectangular WG
65 Figure 3.9(a) : LEAP NFMWIS Scan Results Calibration sample CTA scan results Figure 3.9(b) : LEAP NFMWIS Scan Results Open ended WG S can R esults Notice the apparent circles around what appears to be the sample defects. These functions are caused by the point spread function (PSF) of the scanning tip waveguide
66 aperture. The image clearly shows that shapes are evident but they appear blurre d and unclear. There appears to be a series of circles surrounding each shape of the calibration sample when imaged by the Generation II of the LEAP NFMWIS 3. 3 3 .3 LEAP NFMWIS Generation III Even though the CTA was able to obtain better scanning re sults than the open ended WG, the inner conductor of the coaxial cable was not mechanically solid enough to implement contact scanning. Generation III of the LEAP NFMWIS utilized copper tapered scanning tip s These tips are various diameters and lengths to suit different scan ning test frequencies. Figure 3 .10 shows the collection of the prototyped copper pr obe tips (CPTs) that the LEAP Team has fabricated. Figure 3.10: Copper Probe Tips CPTs are designed to attach to an SMA connector to implement scan testing. The diameter of each probe tip is 4mm, 3mm and 2mm (from left to right). Since the resolution of MWIS depends on the size of the probe tip, LEAP designed probe tips of various sizes. The probe tips in Figure 3.10 are for 3GHz and 10GHz freespace ) scan test frequencies. T he designed length the of each antenna are 1/2 of a wavelength corresponding to the 3GHz and 10 GHz scan frequencies this will direct the EMF
67 at a particular sample location This design feature aids in minimizing the return loss of the antenna. Figure 3.11 shows the return loss of the CPT as it was analyzed with a vector network analyzer between the frequency range of 500 KHz and 3GHz. The expectation is to see the strength of the return ed signal dip at 3GHz, which means the signal, was purely transmitted from the tip at 3GHz as it was designed. However, the return loss measurements indicate that the signal is mo stly transmitted at 2.2GHz. This means the probe tip that LEAP designed is able to transmit and reflect signals, but not at the intended frequency Developing the right CPT is an ongoing process that LEAP continues to work towards. Figure 3.11: CPT Re turn Loss We also did the scanning test using the CPT. We put the ground plate at the bottom of the tip to reduce the noise [pochnak.pdf, Wang.pdf]. Since SMA connectors work better at high frequencies comparatively, we used SMA cables as transmission li nes to transfer the signals. Figure 3.12 shows the new scan setup using the CPT.
68 Figure 3.12: Experimental setup with CPT LEAP conducted several experimental scans of calibration sample with the new CPT and compared the images to the CTA. The results are provided in Figure 3.13 Figure 3.13: Scan result comparison between CPT (left) and CTA (right) The result show s that the shapes from the calibrate sample are clear and also reduce the noise around the shapes, which means the CPT is able to give us a sharper image compare d to the CTA and the rectangular open ended WG LEAP has implemented thus far. The finer tip has a smaller aperture and a smaller PSF. Comparing the CTA imaging results to the CPT results notice that circles or a iry disks caused by the CTA are non
69 existent in the CPT imaging results. The CPT does impose a PSF but it is more confined and provides little blurring when imaging the calibration sample. 3.3 .4 Image Resolution Techniques Figure 3.7 introduced resu lts of the Generation I imaging system using the SA as the DAQ device. N defects and non defects like the results of the CTA or CPT. Through experimentation and analysis it was seen th at the amplitude measurement data set acquired for the calibration test samples has a small dynamic range when scanned The small dynamic range can be due to multiple things, systematic noise due to the mismatched impedance of the cables, connectors and e quipment of the LEAP NFMWIS sample vibration due to positioner movements and the PSF of the open ended WG Small dynamic range can also be attributed to a low SNR of the imaging system. In physical implementations of any MWIS there will be some noise attributed to system signals. These imaging impurities can be filtered out with CPTs or better shielding techniques but noise can also be filtered out with image thresholding techniques. Image thresholding is a technique often applied to image pixels but LEAP decided to try it with the Rx amplitude data acquired with Generation I of the LEAP NFMWIS using the SA as the DAQ device. The goal is to make this distinction as sharp as possible so a binary thresholding technique was applied. Binary thresholding is an image processing technique to improve the contrast between foreground objects and the background in an image. Consider the simple binary thresholding algorithm (3.7)
70 Where O i is the value of the output data point i I i is the value of input data point i and T LOW an d T HIGH are the high and low threshold values. If the data point is within the threshold values it makes that data point appear white in the image ( O i = 1). If the data point falls outside the thresholding values then the algorithm makes the data point appear black in the image ( O i = 0). In image processing a histogram of an image is produced in order to algorithmically determine thresholding values. Using MATLAB a histogram of the acquired Rx amplitude dataset imaged in Figure 3 .14 was produced. Figure 3 .14: Histogram of Gen. I Rx Amplitude Data Acquired with SA Instead of deriving the T LOW and Thigh thresholding values algorithmically, they were derived experimentally based on analysis of the histogram data. The optimum low and high threshold was set to T LOW = 50.05dB and T HIGH = 47.5dB Figure 3 .15 shows a flow chart of the binary thresholding algorithm applied in MATLAB.
71 Figure 3.15 : MATLAB Thresholding Algorithm The binary double thresholding technique was applied to bring the sample defects to the foreground and place all other data points in the background. The binary thresholding results can be viewed in Figure 3.16 Notice how there binary thresholding technique creates a strong contrast between defect and non defect areas. It also helps remove the PSF of the open ended WG. This feasibility study shows the power of imposing image processing techniques in order to optimize the imaging results of the MW IS. It also helps to limit cost and time in refining the MWIS components if sufficient image processing algorithms can be applied.
72 Figure 3.16 : Binary Thresholding Results: (left) Voltage Amplitude Image using LEAP NFMWIS Generation I; (right) Imag e after applying optimized binary thresholding algorithm 3.4 LEAP NFMW PHASE DETECTION APPROACH The purpose of the phase detection approach is to d evelop an approach to measure phase difference between the transmitted (Tx) and received (Rx) signals of the LEAP NFMWIS to see if plotting the phase difference improves system resolution. The difference in phase between transmitted signal (Tx) and the reflected signal (Rx) is a fruitful area for research and investigation besides the magnitude information collected by Approach 1 of the LEAP NFMWIS The goal of Approach 2 of the LEAP NFMWIS is to collect the phase difference information between Tx and the reflected signal Rx. Phase information aids in material characterization and imaging materials with smaller dielectric constants such as composite materi als, liquids or tissue  Composite materials are quickly replacing materials because their strong degradation resistance and light weight. The necessity of imaging liqu ids and tissue is strongly driven by the desire to develop adequate medical microwave imaging applications.
73 3.4.1 Background Information It was thought the phase difference between the transmitted signal, Tx, and received signal, Rx, in the LEAP Ne ar Field Microwave Imaging System (NFMWI) could be acquired using a HP8559A spectrum analyzer. This is not possible because the HP559A is a scalar spectrum analyzer. Scalar spectrum analyzers only measure and report the amplitude vs. frequency of the inp ut signals over a specified frequency span. In the NFMWI system the transmitted signal is a 10GHz sinusoidal signal of the form, (3. 8 ) : pha se of the transmitted signal A : Tx signal amplitude : angular frequency = where f =10GHz (3.9) Vrms: root mean square voltage Vpp: voltage peak to peak Scalar spectrum analyzers often present the amplitude in terms of signal power referenced to 1mW or Decibels milliwatts ( dBm ), ( 3.10 ) R: signal input impedance = (typical) P o : reference power, 1mW for dBm, 1W for dB The amplitude measurement displayed by a scalar spectrum analyzer contains no phase information about the sinusoidal inpu t signal. Therefore, using the HP8559A, only the amplitude, power and frequency of the NFMWI S Rx signal can be extrapolated.
74 3.4.1 .1 HP8559A Phase Measurement Simulation Consider the following feasibility test. A Tektronix AFG3102 arbitrary function generator was used to simulate the Rx signal of the NFMWI system. The function generator was configured to supply a 100MHz, 1 Vpp, 0 phase shift sinusoidal signal. The signal was supplied to an Agilent 8463E scalar spectrum analyzer. Using equations 3.9 and 3.10 the amplitude of the signal is Figure 3.17 shows the screenshots of the function generator signal and the frequency vs. amplitude curve measured by the Agilent 8463E. The red boxes on Figure 3.17 highlight the properties and measurements of the simulated Rx signal on their respective devices. The Agilent 8463E measured an input signal with amplitude of 3.64 dBm and a frequency of 100MHz. Notice, t he screen of the spectrum analyzer reports no information on the phase of the input signal. Figure 3.17 : NFMWI Phase Measurement Simulation: Rx Signal (right); Scalar Spectrum Analyzer Measurement (left) Due to the inverse scattering produced by the test sample the re w ill be a phase difference between the Tx and Rx signals in the NFMWIS In order to simulate this, a phase shift of 90 degrees was added to the signal produced by the function generator. Figure 3.18 shows the screenshots of the function generator signal with added 90 degrees
75 phase shift and the corresponding amplitude vs. frequency curve measured by the Agilent 8463E. The red boxes on Figure 3.18 highlight the properties and measurements of the simulated Rx signal on their respective devices. Though a 90 degrees phase difference was added to the input signal, the spectrum analyzer makes the exact same measurements seen in Figure 3.17 the Rx signal and what was measured by the Agilent 8463E. This measurement is potentially caused by variances in the impedance of the output port of the signal generator and or the power discrepancy could be related to user error. Nevertheless, the function generator was configured to display the power of its output signal seen in Figure 3 19 Figure 3.18: NFMWI S Phase Measurement Simulation: 90 o Phase Shift Rx Signal (left); Scalar Spectrum Analyzer Measurement (right) The Agilent 8463E interprets the simulated Rx signal with and without phase difference exactly the same. The scalar spectrum analyzer does not preserve the phase or temporal info rmation of an input signal. Since the HP8559A is a scalar spectrum analyzer it is required in order to measure the phase information of a test sample scanned by th e NFMWI system.
76 Figure 3.19: Output Signal Power Displayed on the Function Generator 3.4 .2 Analog Mixing Phase Detection Approach Frequency mixing is an a nalog phase detection technique that is used to measure phase difference between two signals with same frequency. For our purpose these two signals are the transmitted signal (Tx) and the reflected signal (Rx). This approach is achieved by feeding these two amplitude limited signals into a product detector and the output of the detector will represent the phase difference between the signals afte r filtering out high frequency harmonics  Figure 3.20 : Schematic of Product Detector ( Analog Multiplier )  Analog mixing can be performed with an RF or microwave mixer. Typically mixers have 2 inputs (LO, RF) and 1 output (IF). In the NFMWIS LO will be generated by Tx, RF will be generate d by Rx and are expected to oscillate the same frequency. When two
77 analog signals are mixed, a voltage proportional to the detected phase difference is generated at the IF output of the mixer (V IF )  (3.11) V 1 and V 2 : amplitudes of Tx and Rx K : gain caused by the analog mixer circuitry Tx Rx ): phase difference between Tx and Rx V IF is a mixed sinusoidal signal that oscillates at twice the frequency of the input signals with a DC offset  The DC component is roughly proportional to the phase error  T he AC signal is composed of the fundamental frequency of Tx and Rx but also possess some higher unwanted harmonics. Mathematically, the frequency harmonics generated by a mixer are, (3.12) m and n: integers For phase detection, the f undamental output frequency (when n=1 and m=1) is desired the existence of all other harmonic terms distorts the IF output si gnal of the mixer causing significant problems in accuracy  Normally, these unwanted frequency harmonics are filte red out by a low pass filter leaving the DC offset: (3.13) Rearranging equation 3.13, t he phase difference between Tx and Rx can be c omputed. (3.14)
78 Generally the response of phase detectors is non linear and repeats over a limited phase range. However the response is usually very nearly linear in a narrow phase range  Two simulations were performed to gain the theoretical and physical understanding of phase detectors utilizing analog mixers These simulations aided in optimizing the LEAP NFMWIS phase detection approach before purchasing the phase detection hardware. 3. 4 .2 .1 MATLAB Phase Detector Simulation An analytical simulation for phase detection using analog mixing was performed using a MATLAB The script Phase_Detector_Simulation .m ( see Appendix A ) generates the following sinusoidal signals to simulate Tx and Rx. Figure 3.21: Plot of Tx and Rx and their frequency spectrums Figure 3.21 compares th e Tx, Rx and their frequency spectrums. The script calculates the product of Tx and Rx which is a sinusoid at twice the fundamental frequency that
79 oscillates with a DC offset proportional to the phase difference between Tx and Rx. The product signal, G x is shown in Figure 3.22 Figure 3.22: Tx and Rx Product Signal and frequency spectrum The script applies a low pass filter to isolate the DC offset of the resultant signal. Using equation 3.14 the phase difference between Tx and Rx was plotted versus the DC offset of the product signal G x (V OG X ) in Figure 3.24
80 Figure 3.24: V OGX vs. Tx and Rx Phase Difference 3. 4 2 .2 LTspice Phase Detector Simulation Using LTs pice, a phase detector circuit was created The circuit consists of 2 RF input signals, 1 RF output s ignal, a double balanced mixer and a first order low pass filter. Figure 3.25 : Double balanced Mixer Phase Detection Circuit In order to measure the phase signal between Tx and Rx they should oscillate at the same frequency, so RO=LO=100MHz. Each signal is co upled into the mixer by a 1:1 transformer to not amplify either signal and to keep both input signals isolated from one
81 another. To remove distortion in the phase detector, a first order low pass filter was add to the IF output of the mixer to remove unwa nted higher order harmonics. A first order low pass filter has the following cut off frequency, (3.15) R 1 : filter resistor C 1 : filter capacitor The cut off frequency of the phase detector circuit in Figure 3.25 f C 159KHz, which is well below the double f requency component 2*f = 200MHz. Using LTSpice the performance of the phase detector circuit was analyzed. Figure 3.26 is a s creenshot of the LTspice simulation circuit. The LO and RF inputs are driven by two sinusoidal 1 VAC @ 100 MHz voltage sources. The 1:1 transformer that couples the LO signal into the phase detector is center tapped to ground so a voltage at half the amplitude of LO is induced into the secondary coil of the transformer. Figure 3.29 shows the unfiltered 1 VAC @ 200 MHz IF output signal from the double balanced mixer. The IF output signal is twice fundamental frequency of t he LO and RF input AC voltage sources. The IF output also oscillates with a DC offset of 250 mV. Figure 3.30 shows the harmonics of the IF output signal and why a low pass should be used in order to isolate the frequency component of interest. In the ca se of phase pass filter that isolates the DC component of the product signal and eliminates all higher order harmonics because they cause signal distortion. The double frequency AC component in IF is filtered out by t he first order low pass filt er (see Figure 3.25 ) leaving the DC offset. The C1 capacitor in the low pass filter takes approximately 5 time
82 of the DC offset. The minimum time it takes for the capacitor to charge up to the DC voltage needs to be considered. The delay between each acquired data point would need The effect the low pass filter has on the mixer IF voltage output is shown in Figure 3.31 Figure 3.26: Screenshot of Double Balanced Mixer B uilt with LTspice Figure 3.27 : LO and RF Phase Detector Input Signals
83 Figure 3.28 : LO Induced Signal into Phase Detector Circuit Figure 3.29 : Phase Detector IF output without Low pass Filter
84 Figure 3.30 : Spectrum of Mixer IF Output Voltage Figure 3.31 : Phase Detector IF output with Low pass Filter The phase difference between the RF and LO signals was varied between 0 to 180 degrees by increments of 9 degrees At each phase incremen t the IF voltage was measured. Figure 3.32 shows the phase detector output voltage measured at each phase
85 increment. The phase difference was calculated using equation 3.14 Figure 3.33 compares the calculated and theoretical phase difference Figure 3.32 : Double balanced Mixer Phase Difference Measurement Figure 3.33 : Detected Phase Differences Actual vs. Calculated -500 -400 -300 -200 -100 0 100 200 300 400 500 0 30 45 60 90 120 135 150 180 IF Voltage [mV] Phase Difference [degrees] Phase Detector Voltage Response Phase Detector Output Voltage 0 20 40 60 80 100 120 140 160 180 200 1 2 3 4 5 6 7 8 9 Phase Difference [degrees] Measurement # Phase Difference: Calculated vs. Theoretical Theoretical Phase Diff. Calc. Phase Diff.
86 Figure 3.34 : MATLAB Phase Detector Product Voltage Results vs. LTspice Double Balanced Mixer IF Voltage Figure 3.34 comp ares the IF output voltages for the MATLAB and LTspice phase detector models for phase differences ranging from 9 to 180 degrees. The two traces in the plot of Figure 3.34 show vertical lines between the IF voltages of each phase detector model for a giv en phase difference between the input signals LO and RF. These lines highlight the difference between the MATLAB and LTspice phase difference simulation models. Part of the disparity is because the LTspice model requires that the voltage sources LO and RF have some internal series resistance and MATLAB does not. Modeling voltage sources with internal series resistance is a real and more accurate assessment. 3.5 LEAP NFMWI PHASE DETECTION SYSTEM After performing two simulations, physical components to implement pha se detection were purchased. Two components were needed, an RF mixer and a l ow pass filter but only the RF mixer has been purchased at this time Distortion plays are large role when -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0 18 36 54 72 90 108 126 144 162 180 IF Voltage [mV] Phase Difference [degrees] Phase Detector Voltage Response: MATLAB Mixer, LTspice Mixer Phase Detector IF Voltage MATLAB Simulation Phase Detector IF Voltage LTSPICE Simulation
87 creatin g systems that can measure small signals. Amplifiers can be used to amplify small signals but the amplifier does not isolate and only amplify the desired signal, it amplifies the signal distortions and noise as well. Riding the measurement signal of dist ortions is key when using RF mixers for phase detection  Several issues arise when using a mixer for phase detection, LO to IF leakage and improper filtration of high order harmonics in the IF output signal. In order to develop an effective phase detection approach, the performance of the RF mixer had to be analyzed. The RF mixer is a SigTek SM1717 RF Mixer. The mixer h as a specified bandwidth of 4 12 GHz for the RF inputs LO and RF. The mixer also has a 7dB conve rsion loss (10*log[Pin/Pout]), this is the amount of loss incurred between the input signal (RF) and output signal (IF). When the mixer was purchased a WAVETEK 809A was used as a 10GHz microwave source in the LEAP NFMWIS but after experimentation this source was proved to be noisy. The WAVETEK 809A was replaced by a more modern Agilent N5181A arbitrary waveform generator a nd the source signal (Tx) was adjusted to 3GHz. Figure 3.35 shows the setup used to make bandwidth measurements of the mixer to see if driving the mixer with a 3GHz source was feasible or would the mixer attenuate the signal too much so phase detection w as not possible.
88 Figure 3.35 : Diagram of Mixer BW measurement setup An Agilent E5062A network analyzer (NA) configured to make throughput measurements (S 12 ) was used to analyze the frequency response of the mixer. To see how much the mixer inputs would attenuate the 3GHz signal and determine if the 3GHz signal generated by the Agilent signal generator could be used to drive the LO input of the mixer during scanning. The Agilent generator was used to generate a +17dBm @ 3GHz microwave signal. Figure 3.36 shows the forward voltage gain from the RF input to the IF output from 300 KHz to 3 GHz. Three frequency points are marked in the figure showing the amplitude of the transmitted signal from the SM1717 RF Mixer Bandwidth Analysis Notice that the amplitu de of the signal is 10. 462 dB (+19.583 dBm) at 3 GHz.
89 Figure 3.36 Forward voltage gain from the RF input to the IF output Before integrating the mixer into the LEAP NFMWIS the mixing behavior of the RF mixer was compared to IF voltage simulation data from the SPICE and MATLAB and simulation. Using the setup in Figure 3.37 the IF voltage output of the mixer was analyzed. A phase difference was generated by adjusting the phase difference on one signal generator and leaving the other one constant at 0 degrees. The phase was adjusted between the range of 0 180 degrees and the resulting IF voltage was measured at 9 degree increments to agree with the MATLAB and LTspice simulations. F igure 3.38 shows the measured IF voltage compared to the IF voltag e seen in the MATLAB and SPICE simulations and Figure 3.39 compares the phase difference approximations between the MATLAB and LTspice simulations to the approximations made with the physical mixer.
90 Figure 3.37: RF Mixer IF Voltage Experiment Setup Figure 3.38 : MATLAB, LTspice and SM1717 RF Mixer IF Voltage Response -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0 18 36 54 72 90 108 126 144 162 180 IF Voltage [mV] RF & LO Phase Difference [degrees] Phase Detector Voltage Response: MATLAB Mixer, LTspice Mixer, SM1717 Mixer Mixer IF Voltage MATLAB (V) Mixer IF Voltage LTSPICE (V) SigTek 1717 Mixer IF Voltage (V)
91 Figure 3.39: MATLAB, LTspice and SM1717 RF Mixer Phase Difference Approximations There are two common noise modes when using analog mixers, signal leakage between the input and output ports of the mixer and harmonic distortion. Signal leakage can occur between the LO and RF port (Isolation LO RF) and between the LO and IF ports (Isolation LO IF). Per the SM717 specification the LO RF isolation is 25dB min. and the LO IF is olation is 20dB min. T he spectrum of the IF output of the mixer was analyzed with a CXA N9000A spectrum analyzer. When two analog AC signals are mixed the output is a mixed signal consisting of an AC signal that oscillates with a DC offset. As mentio ned before, the AC signal is composed o f harmonics of the general form F IF = n F LO F RF Two Agilent NXA signal generators were used to drive the RF and LO input signals to the SM1717 RF mixer. One signal generator was set to output a 17dBm @ 3GHz signal to the LO input ; the other one was set to provide 17dBm @ 1 GHz signal to the RF input 0 20 40 60 80 100 120 140 160 180 0 18 36 54 72 90 108 126 144 162 180 Calculated Phase Difference [degrees] Theoretical Phase Difference [degrees] Calculated Phase Difference: Calculated vs. Theoretica l Calc. Phase Diff. 1717 SigTek Mixer [degrees] Calc. Phase Diff. LTSPICE [degrees] Calc. Phase Diff. MATLAB [degrees]
92 Table 3.1: IF Voltage Frequency Harmonics The output from the mixer was fed to the spectrum analyzer to study the spectrum of the IF voltage output signal. The measured IF signal harmonics are presented in the SA screenshot provided in Figure 3.43 Figure 3.43: SA CXA N9000A display with 3GHz and 1 GHz fed to the mixer inputs 3.6 INITIAL LEAP NFMWI PHASE DETECTION Figure 3.43 shows annotated pictures of the physical LEAP NFMWI Phase De tection S ystem, the block diagram of the measurement setup is located in Figure 3.2 F IF Harmonics Frequency (GHz) 0*F RF + F LO 3 F RF + 0*F LO 1 F RF + F LO 4 F RF F LO 2
93 Figure 3.43: LEAP NFMWI Phase Detection System 3 .6 .1 LEAP NFMWI Phase Detection Measurement Method Here are the initial results from the LEAP NFMWI Phase Detection Approach. Measurements were taken with a coaxial scanning tip waveguide positioned a lift of f setup was determined by measuring the triangle in the middle of the sample (see Figure 3.44 ). The output of the microwave generator was externally set to + 0.9dBm @ 7.33GHz with an analog voltage of +0VDC using an HP DC power s upply (see WAVETEK 907A anual for external control of power output ). Figure 3.44: Mid size Triangle on Aluminum C alibration Sample Mid size triangle
94 The microwave source was routed to a +20dBm amplifier and then split into two signals with a power splitter. One of the signals drives the LO input of the mixer and the other is routed to a directional coupler and fed to the CTA. The Rx signal induced in the CTA from the signal reflection produced by the sample under test is fed to the RF input of the mixer. The mixer takes the products of two Tx and Rx sinusoidal signals and produces a mixed signal at its IF output (see equation 3.1 1 ). The current phase detection setup does not have a low pass filter. LEAP wanted to perform initial testing in order to test the feasibility of the phase detection approach before purchasing additional hardware. Even without a low pass filter, the DC offset that resi des in the mixed signal IF output can be measured with a DMM but there will be distortion in this signal due to the higher order harmonics of the AC signal. This distortion will cause errors in the phase difference calculation but the approx imate phase d ifference between the two analog signals can still be computed using equation 3.14 After preliminary the test results are analyzed and the setup is a little more refined, study of adding a low pass filter to improve phase measurement accuracy will be imp lemented. 3.6 .2 Initial Phase Detection Scan Results Figure 3.45 shows the IF output voltage of the mixer measured by the DMM for the mid size triangle on the calibration sample. Figure 3.46 shows an image of the calculated phase difference using Eq. 2 From the feasibility phase detection scans there is noticeable contrast between the fabricated triangle hole and the surrounding aluminum material. Due to the success of the phase detection feasibility scan taken of the mid size triangle on the calibration sample, a scan of the entire aluminum calibration sample was performed. Figure 3.47 Figure
95 3.48 is an image of the calculated phase difference using Eq. 2 When the phase difference calcu lations were imaged in Figure 3. 4 8 there is minimal contrast between the defects and non defect material. This is because the information was plotted using imagesc function with the hot color map. The imagesc function scales image data to the full range of the hot color map. To improve image contrast, the IF voltage and phase difference data were imaged using the default color map of the imagesc function. The results can be seen in Figures 3.49 and 3.50 Figure 3.45: Phase Detection Feasibility Scan of Mid size Triangle
96 Figure 3.46: Image of Calculated Phase Difference Figure 3.47: Image of Mixer IF Voltage Measurements
97 Figure 3.48 : Image of Calculated Phase Difference Figure 3.49: Image of Mixer IF Voltage Output Default imagesc Color Map
98 Figure 3.50: Image of Calculated Phase Difference Default imagesc Color Map 3 .7 SUMMARY T he major components of the LEAP NFMWIS are a PC, microwave generator, waveguide, DAQ device and X Y positioner The waveguide is used to transmit the microwave radiation to the sample under test, the DAQ device is used to measure the the PC is used to collect the data, post process and g enerate the image. In the LEAP NFMWIS t he waveguide that transmits the microwave radiation also measures the response of the waveguide to the received near field radiation making the NFMWIS a 1 port network. Currently there are two main approaches to optimizing the LEAP NFMWIS setup. Approach 1 is an amplitude detection approach that measures the received DC signal produced in the waveguide scanning tip waveguide to the reflected microwave energy from a test sample in the near field. Approach 2 is a phase detection approach that measures the phase difference between the received AC signal produced in the CTA
99 waveguide versus the transmitted AC signal from the microwave source. Any MWIS requires optimization in order to refine its imaging capabilit y. Chapter 3 shows optimization in the areas of system synchronization, raster scanning algorithms, DAQ algorithm, waveguide design and image post processing techniques. This chapter expresses that MWIS should have the shortest execution time but not at the expense of poor image resolution. It also demonstrates that there is a strong correlation between image resolution and waveguide design. LEAP NFMWIS Gen. I reaffirms that rectangular open ended waveguides are good for microwave imaging applica tions but not for systems that require sub mm resolution. LEAP NFMWIS Gen. II reaffirms that sub mm resolution can be achieved with coaxial scanning tip waveguides. LEAP NFMWIS Gen. III reaffirms that EMF can be localized and directed using a tapered sca nning tip waveguide which generates higher quality images. Chapter 3 also provided insight on the LEAP Teams parallel development process of a phase detection system in hopes of imaging materials with lower dielectric constants (i.e. composite materials, liquids, body tissue). There are a lot of improvements and optimizations that can still be made to the LEAP NFMWIS but the information presented in Chapter 3 shows that the team is headed in a feasible and good direction.
100 CHAPTER IV CONCLUSION 4.1 FUTURE WORK 4.1 .1 Numerical Modeling of LEAP NFMWIS Waveguide The most essential component of any imaging system is the channel which the energy is transferred to the object. In the case of MIS, the waveguide is the channel that transfers EMF int ensity to the sample to be imaged. The more intensity that can be transferred from the source to the object or the higher the efficiency of the system, better the imaging quality. Modeling waveguides using FDTD can be beneficial in deciding what waveguid e shape and material will work for a particular system. Ideally, you want the applied source field to be the field that r eaches the target without loss. P hysically, this is not the case. FDTD can be used to model how the EMF will interact within the wav eguide to determine its efficiency. In summer 2012 a simple FDTD model was constructed on the Generation I LEAP NFMWIS during a NSF Research Internship conducted at the Indian Institute of Technology Madras. 4. 1.1.1 Algorithm Finite Difference Time Domain is a numerical modeling technique for computational electrodynamics. FDTD makes possible algorithmic modeling of electromagnetic wave (EMW) interaction in physical structures. EMW is modeled the same way as acoustic w aves, with the wave equation (see Chapter 2). With given initial conditions the EMF amplitude can be approximated at any point in time and space. Instead of solving complex partial differential equations in order to obtain field intensity, you can apply an
101 FDTD algorithm to reduce solutions to algebraic equations. It does this by systemically turning time dependent Maxwell equations into finite difference equations via Taylor Series Expansion. Mathematically the process is represented with equation 4.1 (4.1) If u is the in put field or microwave source, equation 4.1 explains that the approximation of a wave at any point and time in space is nothing more than the derivative of the source wave at the specified point. Central or finite difference equations describe the velocity or rate of change of a wave as it moves through space. The solutions to these differe nce equations yield an approximation of the EMF i ntensity at particular instance in time and space within the modeled structure. M ost common ly FDTD modeling is performed with Yee C ells and the Yee algorithm n amed after the originator Kane Yee. Yee C ells are a form of model meshing that is used to spatially define the electric and magnetic fields propagating within a structure A cluster of Yee C ells is appropriately termed a Yee M esh. Figure 4.1 shows the geometry of a FDTD Yee C ell in 3D Notice that the Yee C ell resembles a cube that describes the Magnetic field (H field) and Electric field (E field) component s at discrete points along the surface of the cell these points are called nodes (similar to nodes in an electric circuit).
102 Fi gure 4.1: Yee Cell Mesh Grid The E and H fields at each node can be numerically solved using the Yee Algorithm The algorithm yields an approximation of the EM radiation at any point in time and space inside a structure Generally these are the steps to simulate a FDTD Yee A lgorithm: (1) Define computational domain for the simulation in terms of Yee C ells (2) Define the permittivity, permeability and conductivity of each cell within the modelling domain. (3) Excite the Yee Mesh (cluster of Yee Cells) an AC source. (4) Compute the H field vector components in the cell at a given ins tant in time. (5) Compute the E field vector components in the same cell at the next instant in time. (6) Repeat the process over and over again un til the desired transient or steady state electromagnetic fie ld behavior is fully calculated 126.96.36.199 Boundary Conditions Quality FDTD models impose boundary conditions (BCs). Dissimilar material interfaces and sources along boundaries result in discontinuous field behavior. At such
103 boundaries, are meaningless and cannot be used to define the EMF behavior  I nstead, the field behavior is given by the BCs that examine the field vectors themselves at discontinuous boundaries. Accounting for BCs permits the construction of accurate numerical models for diverse structural geometries These BCs describe how the E MF propagates at the walls of a structure and correctly account for the TEM modes that are produced at these boundaries To reduce model complexity, Perfect Magnetic Conducting (PMC) or Perfect Electric Conduction (PEC) are two common BCs that are imposed to develop decent FDTD numerical models. PEC and PMC BCs describe how the E field and H field s flow on the surface boundary of a material. Consider two distinct spaces in a material where the dielectric properties are not the same (anisotropic). PEC and PMC BCs imply that the tangential field component Et and Ht that propagates on the surface between the medium boundary is eq ual to zero (Et = 0 and Ht = 0) and only the normal field components propagate from medium 1 to 2. Figure 4.2: E MFs at material discontinuity : E field at PEC boundary ( left ): H field at PMC boundary ( right )
104 188.8.131.52 Simulation Results A simple FDTD simulation of a parallel plate waveguide was conducted in C++. In the simulation a parallel plate waveguide with interior width = 2.29 cm and height = 1.02 cm was modeled The waveguide was defined by a Yee Mesh of 132 x 20 x 9, 23 ,760 total Yee C ells with 25 cells defined per wavelength. The guide is modeled in a vacuum so the permittivity and permeability equal that of free space o = 8.85 x 10 12 F/m o = 1.26 x 10 6 H/m ) and the wave velocity equals the speed of light ( = 3.00 x 10 8 m/s) The program models EMF interaction within the parallel plate waveguide due to two types of time varying stimulus sources S(t) a sinusoidal pulse or a continuous wave (CW) pulse The source e manates from the x = 0 face of the waveguide T he time varying excitation generates an E and H field inside the waveguide The simulation propagates the EMF for 1,000 steps through the waveguide/meshes which equated to 2.19ns of simulation ti me. Figure 4 .3 summarizes the computational model parameters. Figure 4.3 : FDTD Parallel Plate Computational Parameters
105 Exciting a parallel plate waveguide with a time varying source, induces an E field between the top and bottom plates. Waveguide3DFDTD.cpp simulates EMF behavior to understand how the field propagate s due to the various dielectric properties (i.e. permittivity, permeability) throughout the wave guide. Doing t his gives an idea of how effective and efficient the waveguide wi ll be at transmitting microwave energy The simulation applies the Yee FD TD scheme and first compute s the H field for the cell at each time step and t hen computes the corresponding E field for that cell at the next instance in time for the inner meshes. Finally, the fields at the wall boundar ies of the waveguide are computed imposing PE C BCs on the faces of the Yee M esh that are along the y axis of the waveguide meaning E x and E y com ponents in Figure 4.1 are equal to zero and the Ez component completely reflects because no E field propagates in waveguide wall The initial step in validating the simulation was to ensure the source signals supplied to the waveguide are accurate. In the case of exciting the wave with a sinusoidal pulse, th e source signal is in equation 4.2 (4.2) t: current simulated time (0 to 2.19ns). In the case of a continuous pulse source signal, view equation 4.3. (4.3) C does in order to plot the two stimulus waves, the amplitude versus time data was placed into a text file. The data is then passed to a generates a 1000 x 2 array from the data and produces a plot. Figures 4.2 is a graph of
106 t he sinusoidal pulse and Figure 4.3 depicts the CW pulse. As you can see the two stimulus pulses are accurate. Figure 4.2: Sinusoidal Pulse Stimulus Figure 4.3: Continuous Wave Pulse Stimulus FDTD is all about describing the field behavior within the waveguide. So a visual representation had to be constructed in order to analyze whether the field behavior
107 appeared reasonable. FDTD can describe the EMF intensity at any point and time in space. With a 120x20x9 for the x,y and z dimensions respectively, you can approximated the field amplitude at any combination of these points. The simulation provides field intensity data for six field components (Ex, Ey, Ez, Hx, Hy, Hz) were each component is a 3 dimensional array consisting of the electric field intensity v ector for every point (e.g. Ez[i][j][k ]) in the mesh for that component at a given time step, including the field intensity at the boundaries. Since the source emanates from the x = 0 face of a parallel plate waveguide the only concern is the E field intensity component in the direction of the height of the waveguide, Ez. This is because the sides of the waveguide are open and the guide sits in a vacuum. The intensity of the field a t the boundary walls of the waveguide are computed using PE C boundary conditions. Thus, t he surfaces that make up the top and bottom of the waveguide or Yee M esh (combination of 120x32x9 Yee cells) completely reflect the electromagnetic waves without crea ting phase change in the electric field (E field). After the inner mesh E Field intensity data is com puted, the intensities at the PE C boundaries are computed. The data for each field intensity component, per time step is placed into a text file. The te xt file was then pars ed into 132x20x9 3D arrays via the MATLAB FDTD3D_Sim.m script The MATLAB script also plots each fields text file at each time step one right after another. When FDTD3D_Sim.m is executed the Ez component actually appears to propagate through the waveguide mesh. After multiple simulations, I found out picking a point that is in the center of the mesh/waveguide, Ez, provides the best results.
108 Figure 4.4 displays the 2D (x and y) and 3D (x,y and z) Ez Field intensity in the parallel plate waveguide due to a sinusoidal pulse excitation a t 10GHz. You can see that the s timul us pulse in Figure 4.2 lot of Figure 4.4 In the same plot you see that the sinusoidal pulse appears to reflect and go negative and reflect and return positive again. This behavior is d ue the PEC boundary conditions imposed at the walls along the height of the waveguide implyi ng the Ez field is complet ely reflected. Also in Figure 4.4 is a volumetric representation of the Ez Field intensity at three different time steps: 0.292ns, 1.092ns and 1.53ns. There were some challenges in plotting the 3D Ez field intensity per time st ep. It would be nice to see the field propagate through a volumetric space. This is achievable with additional work. Figure 8 displays the Ez Field intensity in a parallel plate waveguide due to a CW pulse stimulus at 10GHz as well. You can see that the stimulus pulse in Figure 4.3 of Figure 4.5 In the same plot you see that the Ez field produced by the CW pulse appears to peter out periodically through the wavegui de. This is attributed to the stimulus field destructively interfering with the ref lected fields produced by the PEC boundaries. Also in Figure 4.5 is a volumetric representation of the Ez Field intensity at three different time steps: 0.292ns, 1.092ns a nd 1.53ns.
109 Figure 4.4: 2D and 3D Ez Field Due to Sinusoidal Pulse Stimulus Figure 4.5: 2D and 3D Ez Field Due to Continuous Pulse Stimulus 4. 1.1. 4 Simulation Summary FDTD enables computational modeling and simulation of electromagnetic phenomena. This is useful when there is a need to understand how EMF behaves in an object. It reduces complex differential equations, time dependent Maxwell equations, into finite difference equations via Taylor Series Expansion allowing approximations of EMF
110 behavior. FDTD is applicable to a vast set of applications that utilize EMF fields for functionality. Numerical simulations allow fabricators to get an idea of the wa veguide geometry, size and material that works well for a particular imaging system prior to purchasing the actual product. FDTD algorithms are easy to use and easy to understand. It works well for simple applications. For more complex applications or w aveguide geometries the FDTD implementation could take a considerable amount of time. The fact that FDTD requires that the entire computational area be describe by a combination of Yee cells means that very large computational domains can be developed, wh ich results in long simulation times. Future work would be to implement the FDTD algorithms on a GPU to see the benefits in accelerated execution times 4.1.3 Compressed Sensing Reconstruction 184.108.40.206 Background and Challenges The existing data acquistion (DAQ) system using Agilent 34401A DMM approach is not efficient and the sampling rate is limited. The maximum sampling rate of the DMM is 1000 samples per second maximum. Achieving a high sampling rates is a challenge using t he DMM as the DAQ device. In order to overcome this issue, LEAP ordered a high speed National Instrument (NI) 6341 PCIe DAQ module which has a analog input sampling rate of 500,000 samples a second. Theoretically the LEAP NFMWIS can achieve a higher dat a collection rate with the PCIe card because it is 500 times faster than the DMM. Decreased scanning execution times can be achieved with the increased data transfer rate of PCIe bus vesus the universal serial bus (USB) of the DMM. USB has a bit rate of 60 MB/sec, which is slow compared to the 250 MB/sec rate achieved by the
111 PCIe v1.1 compliant bus of the NI 6341 card A NI BNC 2110 Shielded Connector Block was purchased to compliment the PCIe card. This connector block breaks out all of the I/O pins on the PCIe card to BNC female conncetors for easy connection with the ancillary LEAP NFMWIS equipment. 220.127.116.11 Compressed Sensing Approach Compressive sensing is typically an image processing tech nique for efficiently acquiring and reconstructing signals with relatively few measurements. T he LEAP Team is currently working on a continuous DAQ approach to decrease scanning times that will leverage the fast sampling rate of the NI PCIe card. This app roach will perform random sampling at a few discrete locations on the sample under test. C ompressed sensing based reconstruction will be investigated in order to generate microwave images from relatively small amounts of acquired data Implementing this technique in the LEAP NFMWIS would allow for entire microwave images to be constructed by only scanning a small set of discrete locations on the sample under test. If perfected, this approach would significantly reduce the imaging time of the LEAP NFMWIS. 4.2 CLOSING REMARKS Microwave energy was exposed to the average consumer when the microwave oven (microwave) became a common household appliance in the 1970s. The oven generates electromagnetic radiation at micrometer wavelengths or microwaves. The water molecules in the food absorb the microwaves, heating the food and essentially cooking it. Reducing the dependency on the conventional oven, the microwave created an easier way of life for the 70s domestic housewife. The impact o f the microwave oven was
112 energy. Prior to the microwave oven, the most common application of microwaves was in RADAR Microwave radiation is emitted from an anten na to determine the range, altitude, direction, size, shape and velocity of an object in its path. Microwaves either reflect or scatter when they travel from one medium, air, to a second medium, the object material. This is due to the drastic dielectric difference between air and the object material. The dielectric difference between air and metal is considerable. The larger the dielectric differences between two mediums, the larger the amplitude of the reflected microwave radiation. This is why RADAR is superb at detecting aircrafts, tanks, and even missiles. The amount of radiation is commonly classified by the reflection coefficient or the ratio of the reflected microwaves to the transmitted microwaves. Since no material is homogenous and neither are its dielectric properties, m icrowave radiation can exploit these dielectric differences. Research has shown that by measuring the various reflection coefficients across a material, microwave imaging systems (MIS) can evaluate hidden or emb edded flaws within its structure  This thesis shows how this can be accomplished with near field microwave imaging techniques. Near field microwave imaging is concerned with quantitative measurement of the mi crowave s electrodynamic response to materials on length scales far shorter than the free space wavelength of the radiation  Near field microwave imaging techniques are very useful for NDE inspection techniques. Microwave imaging is performed with a MIS that can be used to image ma terial dielectric qualities (permittivity, conductivity, and reflectivity ) MIS generally consists of a microwave source, waveguide, receiver,
113 data acquisition (DAQ) device, and a comp uter to process the data and co nstruct an image of the dielectric tomography of a test object Figure 2.19 is an example of a generic MIS. MIS measurements can be acquired data via a 1 port network     or multi port networks  C hapter 2 introduced t hree distinct near field microwave imaging techniques: open ended waveguides, scanning tip p robes and phase sensitive detection techniques (PSD) Open ended rectangular or coaxial waveguide techniques should be used for imaging applications requiring cm resolution  but scanning tip waveguides should be employed in applications that require sub mm resolution   In order to produce MIS with m resolution it is suggested that the phase of the reflected si gnal induced in the waveguide be measured and compared to the transmitted signal from the MIS source   This chapter also suggests that resolution directly relates to the geometry of the wa veguide and the scanning lift off distance    Reviewing these three near field imaging techniques is useful because it provides background on the motivations behind the LEAP NFMWIS development efforts described in detail in C hapter 2 Early developments of the LEAP NFMWIS used an open ended rectangular waveguide and a 1 port MIS. The reflected si gnals from the test sample were rectified by a crystal detector The root mean square (RMS) voltage of the rectified signals from the crystal detector output was measured with a DMM. This configuration of the LEAP NFMWIS is properly coined Generation I b ecause it was the fir st system developed. Gen. I of the LEAP NFMWIS generated microwave images using software running on a PC to move an x y positioner. The same software would acquire the DMM voltage measurements at the current x y position and would re nder a 2D Plot of the reflected
114 voltage amplitudes when object scanning commenced. The desire of the LEAP NFMWIS is to achieve sub mm resolution and this was not achieved with Generation I. Leveraging the ideas of Wang  an d Pochnak  a scanning tip waveguide technique to spatially confine the EMF radiation to more discrete points on the sample under test was the motivation behind LEAP NFMWIS Generation II. Generation II used a fabricated coax ial scanning tip waveguide employed with the same ancillary equipment used in Generation I. An increase in resolution was witnessed in the images acquired between Generation I and II systems but Generation II images appeared to have noise This noise cou ld originate from several areas, the parasitic capacitance from the close proximity of the sample under test to the waveguide, the waveguide PSF and or lossy connections within the system   Generation III of the LEAP NFMWIS employed a scanning tip waveguide with a tapered tip following the experiments by Pochnak et. al  Noise was reduced by terminating the scanning tip to a coaxial cable using SMA connect ors. Also additional shielding was added to reduce the parasitic capacitance coupling caused by t he sample under test and also isolated the scanning tip from unwanted RF noise in the testing environment. A vast improvement in image contrast and resolutio n has been witnessed with Generation III. T he major components of the LEAP NFMWIS are a PC, microwave generator, waveguide, DAQ device and X Y positioner The waveguide is used to transmit the microwave radiation to the sample under test, the DAQ device is used to measure the the PC is used to collect the data, post process and generate the image. In the LEAP NFMWIS t he waveguide that
115 transmits the microwave radiation also measures th e response of the waveguide to the received near field radiation making the NFMWIS a 1 port network. Currently there are two main approaches to optimizing the LEAP NFMWIS setup. Approach 1 is an amplitude detection approach that measures the recei ved DC signal produced in the waveguide scanning tip to the reflected microwave energy from a test sample in the near field. Approach 2 is a phase detection approach that measures the phase difference between the received AC signal produced in the CTA wav eguide versus the transmitted AC signal from the microwave source. Any MWIS requires optimization in order to refine its imaging capability. Chapter 3 shows optimization in the areas of system synchronization, raster scanning algorithms, DAQ algorithm, w aveguide design and image post processing techniques. Chapter 3 expresses that MWIS should have the shortest execution time but not at the expense of poor image resolution. It also demonstrates that there is a strong correlation between image reso lution and waveguide design. LEAP NFMWIS Gen. I reaffirms that rectangular open ended waveguides are good for microwave imaging applications but not for systems that require sub mm resolution. LEAP NFMWIS Gen. II reaffirms that sub mm resolution can be a chieved with coaxial scanning tip waveguides. LEAP NFMWIS Gen. III reaffirms that EMF can be localized and directed using a tapered scanning tip waveguide which generates higher quality images. Chapter 3 also provided insight on the LEAP Teams parallel development process of a phase detection system in hopes of imaging materials with lower dielectric constants (i.e. composite materials, liquids, body tissue). There are a lot of improvements and optimizations that can still be made to the
116 LEAP NFMWIS but the information presented in Chapter 3 shows that the team is headed in a feasible and good direction. Microwave research is growing in NDE applications due to its, relatively low cost, high imaging contrast and timely results compared to X ray im aging Microwaves exploit the dielectric properties of materials and since materials anisotropic, a dielectric tomography of objects under test can be imaged with MIS. The hardware and software used in the MIS have to be optimized in order to produce th e most efficient system. Fast classifications of objects under test are important but the most important aspect of an MIS factors that needs to be optimized in order to produce a useful MIS. The waveguide used in an MIS should be very application specific, depending on what you want to measure. The waveguide utilized depends on the size and geometry of the object to be imaged. Microwave imaging is ever increasing in popularity due to its broad application range. Microwave imaging has proven effective in imaging concrete   PCB traces  and even body tissue  Some of the LEAP NFMWIS development efforts presented in this thesis are respectable and show signs that the project is moving towards a considerable MIS that will consistently achieve sub mm resolution. 4.3 CONCLUSION The LEAP N FMWIS has accomplished sub mm resolution. Experimental studies presented in this thesis demonstrate that the system image resolution correlates to the sensor aperture size The experimental results presented in Chapter 3 align with the existing research presented in Chapter 2 in that the smaller the aperture, the higher the MIS resolution. Model s assisted in the development of the LEAP NFMWIS phase
117 detection framework The MATLAB and LT spice mixer simulations helped de velop understanding and reaffirm physical behavior of the phase detector element The LEAP NFMWIS amplitude and phase detection approaches have undergone respectable optimization efforts The a mplitude approach has proven good for characterizing material s and the phase detection method is useful for depth estimation and defect profiling. Both approaches have proven feasible at achieving sub mm resolution but there are still areas for improvement. The LEAP Team will continue to develop these areas in order to produce a quality NFMWIS.
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121 APPENDIX A: MATLAB NFMWIS Scanning Code (s can.m ) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Scan.m % %Written By: Jarvis Hill, University of Colorado Denver % %Purpose: This code will perform a scan on test material using the % %LEAP %NFMWIS System set up (Arrick Robotics Scanner + Agilent 3401A % % DMM)Call function by typing 'filename(obj_len,obj_width)' where % % 'filename'= name of MATLAB script and 'obj_len' and 'obj_width' are % % integers representing the dimensions of the test object in inches % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% function main ( obj_len obj_width ) %Decl are table scanning region steps_inch = 180 ; %approx. steps per inch region_width = 33 ; %inches region_length = 33 ; %inches center = 33 ; %# of total steps to length limit length_steps = steps_inch region_length ; %Establish COMs with Arrick Robotics Scanner scanner = init_scanner (); %Establish COMs with Agilent 34405A DMM dmm = init_dmm (); %Home motors 1 & 2 home_motors ( scanner ); %Center test object on scanner center_scanner ( scanner region_length region_width steps_inch ); %Position object for scanning obj_scan_pos ( scanner obj_len obj_width steps_inch ); %Scan test object scan_step = 10 ; %achieves ~1.4mm moves scan_obj ( scanner dmm obj_len obj_width steps_inch scan_step ) %Plot acquired image figure ; imagesc ( data_array ); colormap ( hot ); axis equal ; %Home motor 1 and then motor 2 home_motors ( scanner ); %End communication with scanner fclose ( scanner ); %End communication with dmm fclose ( dmm );
122 end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Function: init_scanner() % %Purpose: Configures serial object for COMs with % % Arrick Robitics C4/MD2 scanning system % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function scanner_device = init_scanner () %Serial COM parameters Port = 'COM1' ; Baud = 9600 ; %Create scanner object scanner_device = serial ( Port 'BaudRate' Baud 'Terminator' 'LF' ); %Open serial comm with scanner (C 4 controller & MD2 Driver) fopen ( scanner_device ); pause ( .1 ) end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% % Function: init_dmm() % %Purpose: Configures visa object for usb COMs % %with Agilent 34405A DMM and begins COMs with deivce % %%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function device = init_dmm () %%Establish serial COM with Instrument (Agilent 34450A) dmmadr = 'USB0::0x0957::0x0618::my52320004::0::INSTR' ; device = visa ( 'agilent' dmmadr ); fopen ( device ) set ( device 'EOSMode' 'read&write' ) set ( device 'EOSCharCode' 'LF' ) fprintf ( device '*CLS;*RST' ); %%Identify Instrument idn = query ( device '*IDN?' ); disp ([ 'IDN? = idn ( 1 : end 1 )]) %%Configure DMM to measure DC voltage fprintf ( device 'CONF:VOLT:DC' ) end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Function: home_motors() % %Purpose: Sends home commands to Arrick Robotics C4 % %controller to home motors 1 & 2. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % function home_motors ( scanner ) %Clear command memory buffer on controller fprintf ( scanner '!1bc' )
123 move_ack = ' %Ensure that motors 1 & 2 start from home position %Set motion parameters %velocity fprintf ( scanner '!1wv1,1000,2000,500' ) pause ( .1 ) %Provide time to set all parameters fprintf ( scanner '!1wv2,1000,2000,500' ) pause ( .1 ) %Provide time to set all parameters %Ensure that motors 1 & 2 start from home position fprintf ( scanner '!1h12' ) pause ( .1 ) while ( move_ack ( end ) ~= 'o' ) move_ack = fscanf ( scanner '%s' 11 ) %It appears # needs to = the exact number of characters in the buffer end %C4 controller command buffer is cleared after every read (fscanf) end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Function: center_scanner() % %Purpose: Places scanning platform and test object % %in center of scanning table. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function center_scanner ( scanner region_length region_width steps_inch ) %Move scanning platform to center of scanning region center_length = ceil ( region_length / 2 ); center_width = ceil ( region_width / 2 ); %Convert numbers to strings str_clength = num2str ( center_length steps_inch ) str_cwidth = num2str ( center_width steps_inch ) %Create scanner center commands with length and width center_cmdlength = strcat ( '!1m1f' str_clength ); center_cmdwidth = strcat ( '!1m2f' str_cwidth ); center_cmdlength = strcat ( center_cmdlength 'n' ); center_cmdwidth = strcat ( center_cmdwidth 'n' ); %Center scanning table in scanning region move_ack = ' fprintf ( scanner center_cmdwidth ) while ( move_ack ( end ) ~= 'o' ) move_ack = fscanf ( scanner '%s' 2 ); %It appears # needs to = the exact number of characters in the buffer end move_ack = ' fprintf ( scanner center_cmdlength ) while ( move_ack ( end ) ~= 'o' ) move_ack = fscanf ( scanner '%s' 2 ); %It appears # needs to = the exact number of characters in the buffer end
124 end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Function: obj_scan_pos() % %Purpose: Positions object under test for raster % % scan by LEAP NFMWI system. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function obj_scan_pos ( scanner obj_len obj_width steps_inch ) %Move scanning platform to top left corner of object scanning r egion center_length = ceil ( obj_len / 2 ); center_width = ceil ( obj_width / 2 ); %Convert numbers to strings str_clength = num2str ( center_length steps_inch ); str_cwidth = num2str ( center_width steps_inch ); %Create scanner center commands with length and width center_cmdlength = strcat ( '!1m1r' str_clength ); center_cmdwidth = strcat ( '!1m2r' str_cwidth ); center_cmdlength = strcat ( center_cmdlength 'n' ); center_cmdwidth = strcat ( center_cmdwidth 'n' ); %Move scanner move_ack = ' fprintf ( scanner center_cmdwidth ) while ( move_ack ( end ) ~= 'o' ) move_ack = fscanf ( scanner '%s' 2 ); end move_ack = ' fprintf ( scanner center_cmdlength ) while ( move_ack ( end ) ~= 'o' ) move_ack = fscanf ( scanner '%s' 2 ); end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Function: scan_obj() % %Purpose: Performs raster scan on test object collecting % %Voltage DC measurement s from DMM DAQ % % Initial LEAP NFMWIS Raster Scan Algorithm % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%% function scan_obj ( scanner dmm obj_len obj_width steps_inch scan_step ) move_ack = ' fprintf ( scanner '!1wv1,400,600,200' ) pause ( .1 ) %Provide time to set all parameters are set fprintf ( scanner '!1wv2,400,600,200' ) pause ( .1 ) while ( move_ack ( end ) ~= 'a' )
125 move_ack = fscanf ( scanner '%s' 2 ) end %Formulate steps in y direction and y start pos. commands x_limit = ceil ((( obj_width steps_inch ))/ scan_step ); y_limit = ceil ((( obj_len steps_inch ))/ scan_step ); y_step = strcat ( '!1m1f' num2str ( scan_step )); y_step = strcat ( y_step 'n' ); x_step = st rcat ( '!1m2f' num2str ( scan_step )); x_step = strcat ( x_step 'n' ); y_start = strcat ( '!1m1r' num2str ( scan_step y_limit )); y_start = strcat ( y_start 'n' ); %Perform scanning of object %motor 2 = x position motor 1 = y position for x = 1 : x_limit for y = 1 : y_limit data_array ( x y ) = str2num ( query ( dmm 'READ?' )); pause(.5) fprintf ( scanner y_step ) while ( move_ack ( end ) ~= 'o' ) move_ack = fscanf ( scanner '%s' 2 ) end end fprintf ( scanner y_start ) pause ( 6 ) while ( move_ack ( end ) ~= 'o' ) move_ack = fscanf ( scanner '%s' 2 ) end fprintf ( scanner x_step ) pause ( .5 ) while ( move_ack ( end ) ~= 'o' ) move_ack = fscanf ( scanner '%s' 2 ) end end end
126 APPENDIX B: LEAP NFMWIS Scanning Algorithm % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%% % Function: scan_obj() % %Purpose: Performs raster scan on test object collecting % %Voltage DC measurement from NFMWI crystal detector. % % Initial LEAP NFMWIS Raster Scan Algorithm % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%% %%%%%%%%%%%%%%% % function data_array = scan_obj ( scanner dmm obj_len obj_width steps_inch scan_step ) %Set motor velocities for performing scan on test object move_ack = ' ; fprintf ( scanner '!1wv1,400,600,200' ); pause ( .1 ) %Provide time to set all parameters are set fprintf ( scanner '!1wv2,400,600,200' ); pause ( .1 ) while ( move_ack ( end ) ~= 'a' ) move_ack = fscanf ( scanner '%s' 2 ); end %Formulate steps in y direction and y start pos. commands x_limit = ceil ((( obj_width steps_inch ))/ scan_step ); y_limit = ceil ((( obj_len steps_inch ))/ scan_step ); y_step_down = strcat ( '!1m1f' num2str ( scan_step )); y_step_down = strcat ( y_step_down 'n' ); y_step_up = strcat ( '!1m1r' num2str ( scan_step )); y_step_up = strcat ( y_step_up 'n' ); x_step = strca t ( '!1m2f' num2str ( scan_step )); x_step = strcat ( x_step 'n' ); tic %Start scan time %Perform scanning of object %motor 2 = x position motor 1 = y position for width = 1 : x_limit %Indexes rows of data array for length = 1 : y_limit %Indexes columns of data array %Measure DC voltage and store into 2D data array %Each time loop iterates a column in the data array gets % populated data_array ( length width ) = str2num ( query ( dmm 'READ?' )); pause ( .1 ) %Check to see if column # even if mod ( width 2 ) == 0 %Move scanner up to next y position fprintf ( scanner y_step_up ); pause ( .05 ) %Wait for scanner to finish move while ( move_ack ( end ) ~= 'o' ) move_ack = fscanf ( scanner '%s' 2 ); end
127 %If column # is odd else %Move scanner down to next y position fprintf ( scanner y_step_down ); pause ( .05 ) %Wait for scanner to finish move while ( move_ack ( end ) ~= 'o' ) move_ack = fscanf ( scanner '%s' 2 ); end end end %Delay at end of each column pause ( .1 ) %Move scanner to next x position fprintf ( scanner x_step ); %Wait for scanner to finish move while ( move_ack ( end ) ~= 'o' ) move_ack = fscanf ( scanner '%s' 2 ); end end %Display how long it took to scan test object scan_stop = toc ; %Represent scan time in hrs:min:secs %Code snippet from Stackoverflow hours = floor ( scan_stop / 3600 ); scan_stop = scan_stop hours 3600 ; mins = floor ( scan_stop / 60 ); secs = scan_stop mins 60 ; hrs = fprintf ( 'Scan Elapsed Time(hrs:mins:secs.ms): %02d:%02d:%05.2f \ n' hours mins secs ); %Notify user that scan is complete fprintf ( 'Scan complete... \ n' ); pause ( 2 ) end