Browsing by Subject "Engineering, Electronics and Electrical"

A 3D microwave imaging system suitable for clinical trials has been developed. The anatomy, histology, and pathology of breast cancer were all carefully considered in the development of this system. The central component of this system is a breast imaging chamber with an integrated 3D antenna array containing 36 custom designed bowtie patch antennas that radiate efficiently into human breast tissue. 3D full-wave finite element method models of this imaging chamber, complete with full antenna geometry, have been developed using Ansoft HFSS and verified experimentally. In addition, an electronic switching system using Gallium Arsenide (GaAs) absorptive RF multiplexer chips, a custom hardware control system with a parallel port interface utilizing TTL logic, and a custom software package with graphical user interface using Java and LabVIEW have all been developed. Finally, modeling of the breast (both healthy and malignant) was done using published data of the dielectric properties of human tissue, confirming the feasibility of cancer detection using this system.

A full custom integrated circuit design of a serial bitstream processor is proposed for remote smart sensor systems. This dissertation describes details of the architectural exploration, circuit implementation, algorithm simulation, and testing results. The design is fabricated and demonstrated to be a successful working processor for basic algorithm functions. In addition, the energy performance of the processor, in terms of energy per operation, is evaluated. Compared to the multi-bit sensor processor, the proposed sensor processor provides improved energy efficiency for serial sensor data processing tasks, and also features low transistor count and area reduction advantages.

Operating in long-term, low data rate sensing environments, the serial bitstream processor developed is targeted at low-cost smart sensor systems with serial I/O communication through wireless links. This processor is an attractive option because of its low transistor count, easy on-chip integration, and programming flexibility for low data duty cycle smart sensor systems, where longer battery life, long-term monitoring and sensor reliability are critical.

The processor can be programmed for sensor processing algorithms such as delta sigma processor, calibration, and self-test algorithms. It also can be modified to utilize Coordinate Rotation Digital Computer (CORDIC) algorithms. The applications of the proposed sensor processor include wearable or portable biomedical sensors for health care monitoring or autonomous environmental sensors.

The primary goals of this study were to demonstrate and fully characterize a microscale ionization source (i.e. micro-ion source) and to determine the validity of impact ionization theory for microscale devices and pressures up to 100 mTorr. The field emission properties of carbon nanotubes (CNTs) along with Micro-Electro-Mechanical Systems (MEMS) design processes were used to achieve these goals. Microwave Plasma-enhanced CVD was used to grow vertically aligned Multi-Walled Carbon Nanotubes (MWNTs) on the microscale devices. A 4-dimensional parametric study focusing on CNT growth parameters confirmed that Fe catalyst thickness had a strong effect on MWNT diameter. The MWNT growth rate was seen to be a strong function of the methane-to-ammonia gas ratio during MWNT growth. A high methane-to-ammonia gas ratio was selected for MWNT growth on the MEMS devices in order to minimize growth time and ensure that the thermal budget of those devices was met.

A CNT-enabled microtriode device was fabricated and characterized. A new aspect of this device was the inclusion of a 10 micron-thick silicon dioxide electrical isolation layer. This thick oxide layer enabled anode current saturation and performance improvements such as an increase in dc amplification factor from 27 to 600. The same 3-panel device was also used as an ionization source. Ion currents were measured in the 3-panel micro-ion source for helium, argon, nitrogen and xenon in the 0.1 to 100 mTorr pressure range. A linear increase in ion current was observed for an increase in pressure. However, simulations indicated that the 3-panel design could be modified to improve performance as well as better understand device behavior. Thus, simulations and literature reports on electron impact ionization sources were used to design a new 4-panel micro-ion source. The 4-panel micro-ion source showed an approximate 10-fold performance improvement compared to the 3-panel ion source device. The improvement was attributed to the increased electron current and improved ion collection efficiency of the 4-panel device. Further, the same device was also operated in a 3-panel mode and showed superior performance compared to the original 3-panel device, mainly because of increased ion collection efficiency.

The effect of voltages applied to the different electrodes in the 4-panel micro-ion source on ion source performance was studied to better understand device behavior. The validity of the ion current equation (which was developed for macroscale ion sources operating at low pressures) in the 4-panel micro-ion source was studied. Experimental ion currents were measured for helium, argon and xenon in the 3 to 100 mTorr pressure range. For comparison, theoretical ion currents were calculated using the ion current equation for the 4-panel micro-ion source utilizing values calculated from SIMION simulations and measured electron currents. The measured ion current values in the 3 to 20 mTorr pressure range followed the calculated ion currents quite closely. A significant deviation was observed in the 20-100 mTorr pressure range. The experimental ion current values were used to develop a corrected empirical model for the 4-panel micro-ion source in this high pressure range (i.e., 3 to 100 mTorr). The role of secondary electrons and electron path lengths at higher pressures is discussed.

Deep brain stimulation (DBS) electrodes are intended to stimulate specific areas of the brain to treat movement disorders including essential tremor, Parkinson's disease and dystonia. An important goal in the design of next generation DBS electrodes is to minimize the power needed to stimulate specific regions of the brain. A reduction in power consumption will prolong battery life and reduce the size of implanted pulse generator. Electrode geometry is one approach to increase the efficiency of neural stimulation and reduce the power required to produce the level of activation required for clinical efficacy.

We first characterized the impedance of the presently used clinical DBS electrodes in vitro and in vivo. Characterization of the electrode-tissue interface impedance is required to quantify the composition of charge transfer to the brain tissue. The composition of charge transfer was dependent on both the current density and the sinusoidal frequency. The assumption of the DBS electrode being ideally polarizable was not valid under clinical stimulating conditions. This implies that irreversible processes that can cause electrode or tissue damage might occur when high charge injection is required for DBS.

Current density distribution is an important factor in determining patterns of neural excitation, tissue damage and electrode corrosion. We developed a recursive simulation scheme to calculate the current density distribution that incorporates the nonlinear electrode-tissue interface into finite-element based models of electrodes. The current density distributions on the electrode surface were strongly dependent on the sinusoidal frequency. The primary current density distribution without including the electrode-tissue interface can be used to estimate neural excitation, tissue damage and electrode corrosion with rectangular stimulus pulses as most of the signal power is at frequencies where the secondary current density distribution matches closely the primary current density distribution.

We designed and analyzed novel electrode geometries to decrease stimulation thresholds, thus reducing power consumption of implanted stimulators. Our hypothesis was that high-perimeter electrode geometries that increase the variation of current density on the electrode surface will generate larger activating functions for surrounding neurons and thereby increase stimulation efficiency. We investigated three classes of electrodes: segmented cylindrical electrodes, serpentine-perimeter planar electrodes, and serpentine-perimeter cylindrical electrodes. An approach that combined finite element models of potentials and cable models of axonal excitation was used to quantify the stimulation efficiency of electrodes with various geometries. Increasing the electrode perimeter increased the electrode efficiency by decreasing stimulation threshold. Both segmentation and serpentine edges provided means to increase the efficiency of stimulation. Novel cylindrical electrodes that combined segmentation with serpentine edges decreased power consumption by ~20% for axons parallel to the electrode and by ~35% for axons perpendicular to the electrode. These electrode designs could potentially prolong the average battery life of deep brain stimulator by more than one year.

Timing-related defects are becoming increasingly important in nanometer-technology integrated circuits (ICs). Small delay variations induced by crosstalk, process variations, power-supply noise, as well as resistive opens and shorts can potentially cause timing failures in a design, thereby leading to quality and reliability concerns. All these effects are noticeable in today's technologies and they are likely to become more prominent in the next-generation process technologies~\cite{itrs2007}.

The detection of small-delay defects (SDDs) is difficult because of the small size of the introduced delay. Although the delay introduced by each SDD is small, the overall impact can be significant if the target path is critical, has low slack, or includes many SDDs. The overall delay of the path may become larger than the clock period, causing circuit failure or temporarily incorrect results. As a result, the detection of SDDs typically requires fault excitation through least-slack paths. However, widely-used automatic test-pattern generation (ATPG) techniques are not effective at exciting small delay defects. On the other hand, the usage of commercially available timing-aware tools is expensive in terms of pattern count inflation and very high test-generation times. Furthermore, these tools do not target real physical defects.

SDDs are induced not only by physical defects, but also by run-time variations such as crosstalk and power-supply noise. These variations are ignored by today's commercial ATPG tools. As a result, new methods are required for comprehensive coverage of SDDs.

Test data volume and test application time are also major concerns for large industrial circuits. In recent years, many compression techniques have been proposed and evaluated using industrial designs. However, these methods do not target sequence- or timing-dependent failures while compressing the test patterns. Since timing-related failures in high-performance integrated circuits are now increasingly dominated by SDDs, it is necessary to develop timing-aware compression techniques.

This thesis addresses the problem of selecting the most effective test patterns for detecting SDDs. A new gate and interconnect delay-defect probability measure is defined to model delay variations for nanometer technologies. The proposed technique intelligently selects the best set of patterns for SDD detection from a large pattern set generated using timing-unaware ATPG. It offers significantly lower computational complexity and it excites a larger number of long paths compared to previously proposed timing-aware ATPG methods. It is shown that, for the same pattern count, the selected patterns are more effective than timing-aware ATPG for detecting small delay defects caused by resistive shorts, resistive opens, process variations, and crosstalk. The proposed technique also serves as the basis for an efficient SDD-aware test compression scheme. The effectiveness of the proposed technique is highlighted for industrial circuits.

In summary, this research is targeted at the testing of SDDs caused by various underlying reasons. The proposed techniques are expected to generate high-quality and compact test patterns for various types of defects in nanometer ICs. The results of this research are expected to provide low-cost and effective test methods for nanometer devices, and they will lead to higher shipped-product quality.

Automated acoustic sensing systems are required to detect, classify and localize acoustic signals in real-time. Despite the fact that humans are capable of performing acoustic sensing tasks with ease in a variety of situations, the performance of current automated acoustic sensing algorithms is limited by seemingly benign changes in environmental or operating conditions. In this work, a framework for acoustic surveillance that is capable of accounting for changing environmental and operational conditions, is developed and analyzed. The algorithms employed in this work utilize non-stationary and nonparametric Bayesian inference techniques to allow the resulting framework to adapt to varying background signals and allow the system to characterize new signals of interest when additional information is available. The performance of each of the two stages of the framework is compared to existing techniques and superior performance of the proposed methodology is demonstrated. The algorithms developed operate on the time-domain acoustic signals in a nonparametric manner, thus enabling them to operate on other types of time-series data without the need to perform application specific tuning. This is demonstrated in this work as the developed models are successfully applied, without alteration, to landmine signatures resulting from ground penetrating radar data. The nonparametric statistical models developed in this work for the characterization of acoustic signals may ultimately be useful not only in acoustic surveillance but also other topics within acoustic sensing.

Data acquisition and diagnostics for chemical and biological analytes are critical to medicine, security, and the environment. Miniaturized and portable sensing systems are especially important for medical and environmental diagnostics and monitoring applications. Chip scale integrated planar photonic sensing systems that can combine optical, electrical and fluidic functions are especially attractive to address sensing applications, because of their high sensitivity, compactness, high surface specificity after surface customization, and easy patterning for reagents. The purpose of this dissertation research is to make progress toward a chip scale integrated sensing system that realizes a high functionality optical system integration with a digital microfluidics platform for medical diagnostics and environmental monitoring.

This thesis describes the details of the design, fabrication, experimental measurement, and theoretical modeling of chip scale optical sensing systems integrated with electrowetting-on-dielectric digital microfluidic systems. Heterogeneous integration, a technology that integrates multiple optical thin film semiconductor devices onto arbitrary host substrates, has been utilized for this thesis. Three different integrated sensing systems were explored and realized. First, an integrated optical sensor based upon the heterogeneous integration of an InGaAs thin film photodetector with a digital microfluidic system was demonstrated. This integrated sensing system detected the chemiluminescent signals generated by a pyrogallol droplet solution mixed with H2O2 delivered by the digital microfluidic system.

Second, polymer microresonator sensors were explored. Polymer microresonators are useful components for chip scale integrated sensing because they can be integrated in a planar format using standard semiconductor manufacturing technologies. Therefore, as a second step, chip scale optical microdisk/ring sensors integrated with digital microfluidic systems were fabricated and measured. . The response of the microdisk and microring sensing systems to the change index of refraction, due to the glucose solutions in different concentrations presented by the digital microfluidic to the resonator surface, were measured to be 95 nm/RIU and 87nm/RIU, respectively. This is a first step toward chip-scale, low power, fully portable integrated sensing systems.

Third, a chip scale sensing system, which is composed of a planar integrated optical microdisk resonator and a thin film InGaAs photodetector, integrated with a digital microfluidic system, was fabricated and experimentally characterized. The measured sensitivity of this sensing system was 69 nm/RIU. Estimates of the resonant spectrum for the fabricated systems show good agreement with the theoretical calculations. These three systems yielded results that have led to a better understanding of the design and operation of chip scale optical sensing systems integrated with microfluidics.

This thesis describes three computational optical systems and their underlying coding strategies. These codes are useful in a variety of optical imaging and spectroscopic applications. Two multichannel cameras are described. They both use a lenslet array to generate multiple copies of a scene on the detector. Digital processing combines the measured data into a single image. The visible system uses focal plane coding, and the long wave infrared (LWIR) system uses shift coding. With proper calibration, the multichannel interpolation results recover contrast for targets at frequencies beyond the aliasing limit of the individual subimages. This thesis also describes a LWIR imaging system that simultaneously measures four wavelength channels each with narrow bandwidth. In this system, lenses, aperture masks, and dispersive optics implement a spatially varying spectral code.

This dissertation describes two computational sensors that were used to demonstrate applications of generalized sampling of the optical field. The first sensor was an incoherent imaging system designed for compressive measurement of the power spectral density in the scene (spectral imaging). The other sensor was an interferometer used to compressively measure the mutual intensity of the optical field (coherence imaging) for imaging through turbulence. Each sensor made anisomorphic measurements of the optical signal of interest and digital post-processing of these measurements was required to recover the signal. The optical hardware and post-processing software were co-designed to permit acquisition of the signal of interest with sub-Nyquist rate sampling, given the prior information that the signal is sparse or compressible in some basis.

Compressive spectral imaging was achieved by a coded aperture snapshot spectral imager (CASSI), which used a coded aperture and a dispersive element to modulate the optical field and capture a 2D projection of the 3D spectral image of the scene in a snapshot. Prior information of the scene, such as piecewise smoothness of objects in the scene, could be enforced by numerical estimation algorithms to recover an estimate of the spectral image from the snapshot measurement.

Hypothesizing that turbulence between the scene and CASSI would introduce spectral diversity of the point spread function, CASSI's snapshot spectral imaging capability could be used to image objects in the scene through the turbulence. However, no turbulence-induced spectral diversity of the point spread function was observed experimentally. Thus, coherence functions, which are multi-dimensional functions that completely determine optical fields observed by intensity detectors, were considered. These functions have previously been used to image through turbulence after extensive and time-consuming sampling of such functions. Thus, compressive coherence imaging was attempted as an alternative means of imaging through turbulence.

Compressive coherence imaging was demonstrated by using a rotational shear interferometer to measure just a 2D subset of the 4D mutual intensity, a coherence function that captures the optical field correlation between all the pairs of points in the aperture. By imposing a sparsity constraint on the possible distribution of objects in the scene, both the object distribution and the isoplanatic phase distortion induced by the turbulence could be estimated with the small number of measurements made by the interferometer.

This dissertation describes three computational sensors. The first sensor is a scanning multi-spectral aperture-coded microscope containing a coded aperture spectrometer that is vertically scanned through a microscope intermediate image plane. The spectrometer aperture-code spatially encodes the object spectral data and nonnegative

least squares inversion combined with a series of reconfigured two-dimensional (2D spatial-spectral) scanned measurements enables three-dimensional (3D) (x, y, λ) object estimation. The second sensor is a coded aperture snapshot spectral imager that employs a compressive optical architecture to record a spectrally filtered projection

of a 3D object data cube onto a 2D detector array. Two nonlinear and adapted TV-minimization schemes are presented for 3D (x,y,λ) object estimation from a 2D compressed snapshot. Both sensors are interfaced to laboratory-grade microscopes and

applied to fluorescence microscopy. The third sensor is a millimeter-wave holographic imaging system that is used to study the impact of 2D compressive measurement on 3D (x,y,z) data estimation. Holography is a natural compressive encoder since a 3D

parabolic slice of the object band volume is recorded onto a 2D planar surface. An adapted nonlinear TV-minimization algorithm is used for 3D tomographic estimation from a 2D and a sparse 2D hologram composite. This strategy aims to reduce scan time costs associated with millimeter-wave image acquisition using a single pixel receiver.

The field of metamaterials has gained much attention within the scientific community over the past decade. With continuing advances and discoveries leading the way to practical applications, metamaterials have earned the attention of technology based corporations and defense agencies interested in their use for next generation devices. With the fundamental physics developed and well understood, current research efforts are driven by the demand for practical applications, with a famous example being the well-known microwave "invisibility cloak." Gaining exotic electromagnetic properties from their structure as opposed to their

intrinsic material composition, metamaterials can be engineered to

achieve tailored responses not available using natural materials. With typical designs incorporating resonant and dispersive elements much smaller than the operating wavelength, a homogenization scheme is possible, which leads to the meaningful interpretation of effective refractive index, and hence electric permittivity and magnetic permeability. The typical metamaterial is composed of arrays of scattering elements embedded in a host matrix. The scattering elements are typically identical, and the electromagnetic properties of the medium can be inferred from the properties of the unit cell. This convenience allows the designer to engineer the effective electromagnetic parameters of the medium by modifying the size, shape, and composition of the unit cell.

This dissertation summarizes several key projects related to my research efforts in metamaterials. The main focus of this dissertation is to develop practical approaches to frequency tunable and reconfigurable metamaterials. Chapter one serves as a background and introduction to the field of metamaterials. The purpose of chapters two, three and four is to develop different methods to realize tunable metamaterials - a broad class of controllable artificially engineered metamaterials. The second chapter develops an approach to characterizing metamaterials loaded with RF MEMS switches. The third chapter examines the effects of loading

metamaterial elements with varactor diodes and tunable ferroelectric

thin film capacitors (BST) for external tuning of the effective medium parameters, and chapter four develops a more advanced method to control the response of metamaterials using a digitally addressable control network. The content of these chapters leads up to an interesting application featured in chapter five - a reconfigurable frequency selective surface utilizing tunable and digitally addressable tunable metamaterials. The sixth and final chapter summarizes the dissertation and offers suggestions for future work in tunable and reconfigurable metamaterials. It is my hope that this dissertation will provide the foundation and motivation for new researchers in the field of metamaterials. I am confident that the reader will gain encouragement from this work with the understanding that very interesting and novel practical devices can be created using metamaterials. May this work be of aid and motivation to their research pursuits.

Quantum Dot Infrared Photodetectors (QDIPs) are important alternatives to conventional infrared photodetectors with high potential to provide required detector performance, such as higher temperature operation and multispectral response, due to the 3-D quantum confinement of electrons, discrete energy levels, and intrinsic response to perpendicular incident light due to selection rules. However, excessive dark current density, which causes QDIPs to underperform theoretical predictions, is a limiting factor for the advancement of QDIP technologies. The purpose of this dissertation research is to achieve a better understanding of dopant incorporation into the active region of QDIPs, which is directly related to dark current control and spectral response. From this dissertation research, doping related dipole fields are found to be responsible for excessive dark current in QDIPs.

InAs/GaAs QDIPs were grown using solid source molecular beam epitaxy (MBE) with different doping conditions. The QDIPs were optically characterized using photoluminescence and Fourier transform infrared (FT-IR) spectroscopy. Devices were fabricated using standard cleanroom fabrication procedures. Dark current and capacitance measurements were performed under different temperature to reveal electronic properties of the materials and devices. A novel scanning capacitance microscopy (SCM) technique was used to study the band structure and carrier concentration on the cross section of a quantum dot (QD) heterostructure. In addition, dark current modeling and bandstructure calculations were performed to verify and better understand experimental results.

Two widely used QDIP doping methods with different doping concentrations have been studied in this dissertation research, namely direct doping in InAs QD layer, and modulation doping in the GaAs barrier above InAs QD layer. In the SCM experiment, electron redistribution has been observed due to band-bending in the modulation-doping region, while there is no band-bending observed in directly doped samples. A good agreement between the calculated bandstructure and experimental results leads to better understanding of doping in QD structures. The charge filling process in QDs has been observed by an innovative polarization-dependent FT-IR spectroscopy. The red-shift of QD absorbance peaks with increasing electron occupation supports a miniband electronic configuration for high-density QD ensembles. In addition, the FT-IR measurement indicates the existence of donor-complex (DX) defect centers in Si-doped QDIPs. The existence of DX centers and related dipole fields have been confirmed by dark current measurements to extract activation energies and by photocapacitance quenching measurements.

With the understanding achieved from experimental results, a further improved dark current model has been developed based on the previous model originally established by Ryzhii and improved by Stiff-Roberts. In the model described in this dissertation, two new factors have been considered. The inclusion of background drift current originating from Si shallow donors in the low bias region results in excellent agreement between calculated and measured dark currents at different temperatures, which has not been achieved by previous models. A very significant effect has been observed in that dark current leakage occurs due to the dipole field caused by doping induced charge distribution and impact-ionized DX centers.

Last but not least, QDIPs featuring the dipole interface doping (DID) method have been designed to reduce the dark current density without changing the activation energy (thus detection wavelength) of QDIPs. The DID samples involve an InAs QD layer directly-doped by Si, as well as Be doping in the GaAs barrier on both sides of the QD layer. The experimental result shows the dark current density has been significantly reduced by 104 times without any significant change to the corresponding activation energy. However, the high p-type doping in the GaAs barrier poses a challenge in that the Fermi level is reduced to be well below the QD energy states. High p-type doping is reported to reduce the dark current, photocurrent and the responsivity of the devices.

To conclude, it is significant to identify to effect of Si-induced defect centers on QDIP dark currents. The subsequent study reveals doping induced dipole fields can have significant effects on QDIP device performance, for example, causing charge leakage from QDs and reducing activation energy, thereby increasing dark current density. The DID approach developed in this work is a promising approach that could help address these issues by using controlled dipole fields to reduce dark current density without changing the minimum detectable energy of QDIPs.

Due to the increasing cost of the enhancement techniques in current projection lithographic techniques and the required time in developing new technology's

as feasible manufacturing technology, EUVL is considered as the leading candidate for production of 45 nm node and less. In EUVL, mask is held electrostatically against chuck. This electrostatic chucking process affects the nonflatness of the mask due to contact interaction and the voltage force between the mask and chuck. A fundamental understanding of chucking phenomenon is required to realize the SEMI P37 and SEMI P40 stringent flatness requirements.

The primary challenge is to understand and characterize the ability of electrostatic chucking phenomenon to acheive consistent and reliable shapes of chucked masks.

The objective of this thesis is to study the effect of initial nonflatness of mask and chuck, chucking voltage, chuck and mask dimension and gravity on the final nonflatness of the mask A finite element model of the mask and chuck with initial nonflat surface is developed. To predict the final nonflatness of the mask frontside with nm accuracy, the contact interaction between mask and chuck is modeled using van der Waals forces. These results are compared with penalty method for the runtime and accuracy of results

Advancements of the semiconductor technology opened a new era in

wireless communications which led manufacturers to produce faster,

more functional devices in much smaller sizes. However, testing

these devices of today's technology became much harder and expensive

due to the complexity of the devices and the high operating speeds.

Moreover, testing these devices becomes more important since decreasing

feature sizes increase the probability of parametric and catastrophic

faults because of the severe effects of process variations. Manufacturers

have to increase their test budgets to address quality and reliability

concerns. In the radio frequency (RF) domain, overall test cost are higher

due to equipment costs, test development and test time costs. Advanced

circuit integration, which integrates various analog and digital circuit

blocks into single device, increases test costs further because of the

additional tests requiring new test setups with extra test equipments.

Today's RF transceiver circuits contain many analog and digital circuit

blocks, such as synthesizers, data converters and the analog RF front-end

leading to a mixed signal device. Verification of the specifications and

functionality of each circuit block and the overall transceiver require

RF instrumentation and lengthy test routines. In this dissertation, we

propose efficient component and system level test methods for RF

transceivers which are low cost alternatives to traditional tests.

In the first component level test, we focus on in-band phase noise of the

phase locked loops (PLL). Most on-chip self-test methods for PLLs aim at

measuring the timing jitter that may require precise reference clocks and/or

additional computation of measured specs. We propose a built in test (BiT)

circuit to perform a go/no-go test for in-band PLL phase noise. The proposed

circuit measures the band-limited noise power at the input of the voltage

controlled oscillator (VCO). This noise power is translated as the high

frequency in-band phase noise at the output of the PLL. Our circuit contains

a self calibration sequence based on a simple sinusoidal input signal to make

it robust with respect to process variations.

The second component level test is a built in self test (BiST) scheme

proposed for analog to digital converters (ADC) based on a linear ramp

generator and efficient output analysis. The proposed analysis method is

an alternative to histogram based analysis techniques to provide test time

improvements, especially when the resources are scarce. In addition to the

measurement of differential nonlinearity (DNL) and integral nonlinearity

(INL), non-monotonic behavior of the ADC can also be detected with the

proposed technique. The proposed ramp generator has a high linearity

capable of testing 13-bit ADCs.

In the proposed system level test methods, we utilize the loop-back

configuration to eliminate the need for an RF instrument. The first loop-back

test method, which is proposed for wafer level test of direct conversion

transceivers, targets catastrophic and large parametric faults. The use of

intermediate frequencies (IF) generates a frequency offset between the transmit

and receive paths and prevents a direct loop-back connection. We overcome this

problem by expanding the signal bandwidth through saturating the receive path

composed of low noise amplifier (LNA) and mixer. Once the dynamic range of the

receiver path is determined, complete transceiver can be tested for catastrophic

signal path faults by observing the output signal. A frequency spectrum

envelope signature technique is proposed to detect large parametric faults.

The impact of impairments, such as transmitter receiver in-phase/quadrature

(I/Q) gain and phase mismatches on the performance have become severe due to

high operational speeds and continuous technology scaling. In the second system

level loop-back test method, we present BiST solutions for quadrature modulation

transceiver circuits with quadrature phase shift keying (QPSK) and Gaussian

minimum shift keying (GMSK) baseband modulation schemes. The BiST methods

use only transmitter and receiver baseband signals for test analysis. The

mapping between transmitter input signals and receiver output signals are

used to extract impairment and nonlinearity parameters separately with the

help of signal processing methods and detailed nonlinear system modeling.

The last system level test proposed in this dissertation combines the benefits

of loop-back and multi-site test approaches. In this test method, we present

a 2x-site test solution for RF transceivers. We perform all operations on

communication standard-compliant signal packets, thereby putting the device

under the normal operating conditions. The transmitter on one device under

test (DUT) is coupled with a receiver on another DUT to form a complete TX-RX

path. Parameters of the two devices are decoupled from one another by carefully

modeling the system into a known format and using signal processing techniques.

This dissertation examines the use of artificial structured materials -- known as metamaterials -- in two antenna applications in which conventional dielectric materials are otherwise used. In the first application, the use of metamaterials to improve the impedance matching of planar phased array antennas over a broad range of scan angles is explored. A phased array antenna is composed of an array of antenna elements and enables long-distance signal propagation by directional radiation. The direction of signal propagation is defined as the scan angle. The power transmission ratio of a phased array is the ratio of the radiated power to the input power, and depends on the scan angle. The variation in the power transmission ratio is due to the different mutual coupling contributions between antenna elements at different scan angles. An optimized stack of dielectric layers, known as a wide-angle impedance matching layer (WAIM), is used to optimize the power transmission ratio profile over a broad range of scan angles. In this work, the use of metamaterials to design anisotropic WAIMs with access to a larger range of constitutive parameters -- including magnetic permeability -- to offer an improved power transmission ratio at a broad range of scan angles is investigated.

In the second antenna application, a strategy to create maximally transmissive and minimally reflective electromagnetic radome materials using embedded metamaterial inclusions is introduced. A radome is a covering used to protect an antenna from weather elements or provide structural function such as the prevention of aerodynamic drag. A radome should be made from a fully transparent and non-refractive material so that radiated fields from and to the enclosed antenna are not disrupted. The aim of this research was to demonstrate that embedded metamaterial inclusions can be used to isotropically adjust the dielectric properties of a composite material to a desired value. This strategy may lead to the creation of a structural material with electromagnetic properties close to air, thus reducing the detrimental scattering effects often associated with conventional radome materials.

Chapter 1 introduces the concept of metamaterials and discusses the use of subwavelength metallic structures to artificially engineer constitutive parameters such as permeability of permittivity. In Chapter 2, the analytical formulations that enable the characterization of the transmission performance of a planar phased array covered with anisotropic impedance matching layers are developed. Chapter 3 discusses the design rules that must govern the design parameters of anisotropic WAIMs realizable using metamaterials, and also presents examples of anisotropic impedance matching layers that provide a maximum power transmission ratio for most scan angles. In addition, numerical and experimental results on a metamaterial placed over a phased array are presented. In Chapter 4, the feasibility of using metamaterials to realize a minimally transparent and fully transmissive radome material is numerically investigated. In Chapter 5, experimental results that corroborate earlier numerical simulation results are analyzed.

A series of fusion techniques are developed and applied to EEG and pupillary recording analysis in a rapid serial visual presentation (RSVP) based image triage task, in order to improve the accuracy of capturing single-trial neural/pupillary signatures (patterns) associated with visual target detection.

The brain response to visual stimuli is not a localized pulse, instead it reflects time-evolving neurophysiological activities distributed selectively in the brain. To capture the evolving spatio-temporal pattern, we divide an extended (``global") EEG data epoch, time-locked to each image stimulus onset, into multiple non-overlapping smaller (``local") temporal windows. While classifiers can be applied on EEG data located in multiple local temporal windows, outputs from local classifiers can be fused to enhance the overall detection performance.

According to the concept of induced/evoked brain rhythms, the EEG response can be decomposed into different oscillatory components and the frequency characteristics for these oscillatory components can be evaluated separately from the temporal characteristics. While the temporal-based analysis achieves fairly accurate detection performance, the frequency-based analysis can improve the overall detection accuracy and robustness further if frequency-based and temporal-based results are fused at the decision level.

Pupillary response provides another modality for a single-trial image triage task. We developed a pupillary response feature construction and selection procedure to extract/select the useful features that help to achieve the best classification performance. The classification results based on both modalities (pupillary and EEG) are further fused at the decision level. Here, the goal is to support increased classification confidence through inherent modality complementarities. The fusion results show significant improvement over classification results using any single modality.

For crucial image triage tasks, multiple image analysts could be asked to evaluate the same set of images to improve the probability of detection and reduce the probability of false positive. We observe significant performance gain by fusing the decisions drawn by multiple analysts.

To develop a practical real-time EEG-based application system, sometimes we have to work with an EEG system that has a limited number of electrodes. We present methods of ranking the channels, identifying a reduced set of EEG channels that can deliver robust classification performance.

In the modern sensing environment, large numbers of sensor tasking decisions must be made using an increasingly diverse and powerful suite of sensors in order to best fulfill mission objectives in the presence of situationally-varying resource constraints. Sensor management algorithms allow the automation of some or all of the sensor tasking process, meaning that sensor management approaches can either assist or replace a human operator as well as ensure the safety of the operator by removing that operator from a dangerous operational environment. Sensor managers also provide improved system performance over unmanaged sensing approaches through the intelligent control of the available sensors. In particular, information-theoretic sensor management approaches have shown promise for providing robust and effective sensor manager performance.

This work develops information-theoretic sensor managers for a general static target detection problem. Two types of sensor managers are developed. The first considers a set of discrete objects, such as anomalies identified by an anomaly detector or grid cells in a gridded region of interest. The second considers a continuous spatial region in which targets may be located at any point in continuous space. In both types of sensor managers, the sensor manager uses a Bayesian, probabilistic framework to model the environment and tasks the sensor suite to make new observations that maximize the expected information gain for the system. The sensor managers are compared to unmanaged sensing approaches using simulated data and using real data from landmine detection and unexploded ordnance (UXO) discrimination applications, and it is demonstrated that the sensor managers consistently outperform the unmanaged approaches, enabling targets to be detected more quickly using the sensor managers. The performance improvement represented by the rapid detection of targets is of crucial importance in many static target detection applications, resulting in higher rates of advance and reduced costs and resource consumption in both military and civilian applications.

It is well established that cochlear implants (CIs) are able to provide many users with excellent speech recognition ability in quiet conditions; however, the ability to correctly identify speech in noisy conditions or appreciate music is generally poor for implant users with respect to normal-hearing listeners. This discrepancy has been hypothesized to be in part a function of the relative decrease in spectral information available to implant users (Rubinstein and Turner, 2003; Wilson et al., 2004). One method that has been proposed for increasing the amount of spectral information available to CI users is to include time-varying stimulation rate in addition to changes in the place of stimulation. However, previous implementations of multi-rate strategies have failed to result in an improvement in speech recognition over the clinically available, fixed-rate strategies (Fearn, 2001; Nobbe, 2004). It has been hypothesized that this lack of success was due to a failure to consider the underlying perceptual responses to multi-rate stimulation.

In this work, psychophysical experiments were implemented with the goal of achieving a better understanding of the interaction of place and rate of stimulation and the effects of duration and context on CI listeners' ability to detect changes in stimulation rate. Results from those experiments were utilized in the implementation of a tuned multi-rate sound processing strategy for implant users in order to potentially ``tune" multi-rate strategies and improve speech recognition performance.

In an acute study with quiet conditions, speech recognition performance with a tuned multi-rate implementation was better than performance with a clinically available, fixed-rate strategy, although the difference was not statistically significant. These results suggest that utilizing time-varying pulse rates in a subject-specific implementation of a multi-rate algorithm may offer improvements in speech recognition over clinically available strategies. A longitudinal study was also performed to investigate the potential benefit from training to speech recognition. General improvements in speech recognition ability were observed as a function of time; however, final scores with the tuned multi-rate algorithm never surpassed performance with the fixed-rate algorithm for noisy conditions.

The ability to improve upon speech recognition scores for quiet conditions with respect to the fixed-rate algorithm suggests that using time-varying stimulation rates potentially provides additional, usable information to listeners. However, performance with the fixed-rate algorithm proved to be more robust to noise, even after three weeks of training. This lack of robustness to noise may be in part a result of the frequency estimation technique used in the multi-rate strategy, and thus more sophisticated techniques for real-time frequency estimation should be explored in the future.

This thesis addresses the problem of source localization and time-varying spatial spectrum estimation with maneuverable arrays. Two applications, each having different environmental assumptions and array geometries, are considered: 1) passive broadband source localization with a rigid 2-sensor array in a shallow water, multipath environment and 2) time-varying spatial spectrum estimation with a large, flexible towed array. Although both applications differ, the processing scheme associated with each is designed to exploit array maneuverability for improved localization and detection performance.

In the first application considered, passive broadband source localization is accomplished via time delay estimation (TDE). Conventional TDE methods, such as the generalized cross-correlation (GCC) method, make the assumption of a direct-path signal model and thus suffer localization performance loss in shallow water, multipath environments. Correlated multipath returns can result in spurious peaks in GCC outputs resulting in large bearing estimate errors. A new algorithm that exploits array maneuverability is presented here. The multiple orientation geometric averaging (MOGA) technique geometrically averages cross-correlation outputs to obtain a multipath-robust TDE. A broadband multipath simulation is presented and results indicate that the MOGA effectively suppresses correlated multipath returns in the TDE.

The second application addresses the problem of field directionality mapping (FDM) or spatial spectrum estimation in dynamic environments with a maneuverable towed acoustic array. Array processing algorithms for towed arrays are typically designed assuming the array is straight, and are thus degraded during tow ship maneuvers. In this thesis, maneuvering the array is treated as a feature allowing for left and right disambiguation as well as improved resolution towards endfire. The Cramer Rao lower bound is used to motivate the improvement in source localization which can be theoretically achieved by exploiting array maneuverability. Two methods for estimating time-varying field directionality with a maneuvering array are presented: 1) maximum likelihood estimation solved using the expectation maximization (EM) algorithm and 2) a non-negative least squares (NNLS) approach. The NNLS method is designed to compute the field directionality from beamformed power outputs, while the ML algorithm uses raw sensor data. A multi-source simulation is used to illustrate both the proposed algorithms' ability to suppress ambiguous towed-array backlobes and resolve closely spaced interferers near endfire which pose challenges for conventional beamforming approaches especially during array maneuvers. Receiver operating characteristics (ROCs) are presented to evaluate the algorithms' detection performance versus SNR. Results indicate that both FDM algorithms offer the potential to provide superior detection performance in the presence of noise and interfering backlobes when compared to conventional beamforming with a maneuverable array.