Matching Items (18)
Description
Asymptotic and Numerical methods are popular in applied electromagnetism. In this work, the two methods are applied for collimated antennas and calibration targets, respectively. As an asymptotic method, the diffracted Gaussian beam approach (DGBA) is developed for design and simulation of collimated multi-reflector antenna systems, based upon Huygens principle and independent Gaussian beam expansion, referred to as the frames. To simulate a reflector antenna in hundreds to thousands of wavelength, it requires 1E7 - 1E9 independent Gaussian beams. To this end, high performance parallel computing is implemented, based on Message Passing Interface (MPI). The second part of the dissertation includes the plane wave scattering from a target consisting of doubly periodic array of sharp conducting circular cones by the magnetic field integral equation (MFIE) via Coiflet based Galerkin's procedure in conjunction with the Floquet theorem. Owing to the orthogonally, compact support, continuity and smoothness of the Coiflets, well-conditioned impedance matrices are obtained. Majority of the matrix entries are obtained in the spectral domain by one-point quadrature with high precision. For the oscillatory entries, spatial domain computation is applied, bypassing the slow convergence of the spectral summation of the non-damping propagating modes. The simulation results are compared with the solutions from an RWG-MLFMA based commercial software, FEKO, and excellent agreement is observed.
ContributorsWang, Le, 1975- (Author) / Pan, George (Thesis advisor) / Yu, Hongyu (Committee member) / Aberle, James T., 1961- (Committee member) / Diaz, Rodolfo (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2012
Description
In many systems, it is difficult or impossible to measure the phase of a signal. Direct recovery from magnitude is an ill-posed problem. Nevertheless, with a sufficiently large set of magnitude measurements, it is often possible to reconstruct the original signal using algorithms that implicitly impose regularization conditions on this ill-posed problem. Two such algorithms were examined: alternating projections, utilizing iterative Fourier transforms with manipulations performed in each domain on every iteration, and phase lifting, converting the problem to that of trace minimization, allowing for the use of convex optimization algorithms to perform the signal recovery. These recovery algorithms were compared on a basis of robustness as a function of signal-to-noise ratio. A second problem examined was that of unimodular polyphase radar waveform design. Under a finite signal energy constraint, the maximal energy return of a scene operator is obtained by transmitting the eigenvector of the scene Gramian associated with the largest eigenvalue. It is shown that if instead the problem is considered under a power constraint, a unimodular signal can be constructed starting from such an eigenvector that will have a greater return.
ContributorsJones, Scott Robert (Author) / Cochran, Douglas (Thesis director) / Diaz, Rodolfo (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2014-05
Description
Programmable Metallization Cell (PMC) is a resistance-switching device based on migration of nanoscale quantities of cations in a solid electrolyte and formation of a conducting electrodeposit by the reductions of these cations. This dissertation presents electrical characterization results on Cu-SiO2 based PMC devices, which due to the na- ture of materials can be easily integrated into the current Complimentary metal oxide semiconductor (CMOS) process line. Device structures representing individual mem- ory cells based on W bottom electrode and n-type Si bottom electrode were fabricated for characterization. For the W bottom electrode based devices, switching was ob- served for voltages in the range of 500mV and current value as low as 100 nA showing the electrochemical nature and low power potential. The ON state showed a direct de- pendence on the programming current, showing the possibility of multi-bit storage in a single cell. Room temperature retention was demonstrated in excess of 105 seconds and endurance to approximately 107 cycles. Switching was observed for microsecond duration 3 V amplitude pulses. Material characterization results from Raman, X-ray diffraction, Rutherford backscattering and Secondary-ion mass spectroscopy analysis shows the influence of processing conditions on the Cu concentration within the film and also the presence of Cu as free atoms. The results seemed to indicate stress-induced void formation in the SiO2 matrix as the driving mechanism for Cu diffusion into the SiO2 film. Cu/SiO2
Si based PMC devices were characterized and were shown to have inherent isolation characteristics, proving the feasibility of such a structure for a passive array. The inherent isolation property simplifies fabrication by avoiding the need for a separate diode element in an array. The isolation characteristics were studied mainly in terms of the leakage current. The nature of the diode interface was further studied by extracting a barrier potential which shows it can be approximated to a Cu-nSi metal semiconductor Schottky diode.
Si based PMC devices were characterized and were shown to have inherent isolation characteristics, proving the feasibility of such a structure for a passive array. The inherent isolation property simplifies fabrication by avoiding the need for a separate diode element in an array. The isolation characteristics were studied mainly in terms of the leakage current. The nature of the diode interface was further studied by extracting a barrier potential which shows it can be approximated to a Cu-nSi metal semiconductor Schottky diode.
ContributorsPuthenthermadam, Sarath (Author) / Kozicki, Michael N (Thesis advisor) / Diaz, Rodolfo (Committee member) / Schroder, Dieter K. (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2011
Description
As integrated technologies are scaling down, there is an increasing trend in the
process,voltage and temperature (PVT) variations of highly integrated RF systems.
Accounting for these variations during the design phase requires tremendous amount
of time for prediction of RF performance and optimizing it accordingly. Thus, there
is an increasing gap between the need to relax the RF performance requirements at
the design phase for rapid development and the need to provide high performance
and low cost RF circuits that function with PVT variations. No matter how care-
fully designed, RF integrated circuits (ICs) manufactured with advanced technology
nodes necessitate lengthy post-production calibration and test cycles with expensive
RF test instruments. Hence design-for-test (DFT) is proposed for low-cost and fast
measurement of performance parameters during both post-production and in-eld op-
eration. For example, built-in self-test (BIST) is a DFT solution for low-cost on-chip
measurement of RF performance parameters. In this dissertation, three aspects of
automated test and calibration, including DFT mathematical model, BIST hardware
and built-in calibration are covered for RF front-end blocks.
First, the theoretical foundation of a post-production test of RF integrated phased
array antennas is proposed by developing the mathematical model to measure gain
and phase mismatches between antenna elements without any electrical contact. The
proposed technique is fast, cost-efficient and uses near-field measurement of radiated
power from antennas hence, it requires single test setup, it has easy implementation
and it is short in time which makes it viable for industrialized high volume integrated
IC production test.
Second, a BIST model intended for the characterization of I/Q offset, gain and
phase mismatch of IQ transmitters without relying on external equipment is intro-
duced. The proposed BIST method is based on on-chip amplitude measurement as
in prior works however,here the variations in the BIST circuit do not affect the target
parameter estimation accuracy since measurements are designed to be relative. The
BIST circuit is implemented in 130nm technology and can be used for post-production
and in-field calibration.
Third, a programmable low noise amplifier (LNA) is proposed which is adaptable
to different application scenarios depending on the specification requirements. Its
performance is optimized with regards to required specifications e.g. distance, power
consumption, BER, data rate, etc.The statistical modeling is used to capture the
correlations among measured performance parameters and calibration modes for fast
adaptation. Machine learning technique is used to capture these non-linear correlations and build the probability distribution of a target parameter based on measurement results of the correlated parameters. The proposed concept is demonstrated by
embedding built-in tuning knobs in LNA design in 130nm technology. The tuning
knobs are carefully designed to provide independent combinations of important per-
formance parameters such as gain and linearity. Minimum number of switches are
used to provide the desired tuning range without a need for an external analog input.
process,voltage and temperature (PVT) variations of highly integrated RF systems.
Accounting for these variations during the design phase requires tremendous amount
of time for prediction of RF performance and optimizing it accordingly. Thus, there
is an increasing gap between the need to relax the RF performance requirements at
the design phase for rapid development and the need to provide high performance
and low cost RF circuits that function with PVT variations. No matter how care-
fully designed, RF integrated circuits (ICs) manufactured with advanced technology
nodes necessitate lengthy post-production calibration and test cycles with expensive
RF test instruments. Hence design-for-test (DFT) is proposed for low-cost and fast
measurement of performance parameters during both post-production and in-eld op-
eration. For example, built-in self-test (BIST) is a DFT solution for low-cost on-chip
measurement of RF performance parameters. In this dissertation, three aspects of
automated test and calibration, including DFT mathematical model, BIST hardware
and built-in calibration are covered for RF front-end blocks.
First, the theoretical foundation of a post-production test of RF integrated phased
array antennas is proposed by developing the mathematical model to measure gain
and phase mismatches between antenna elements without any electrical contact. The
proposed technique is fast, cost-efficient and uses near-field measurement of radiated
power from antennas hence, it requires single test setup, it has easy implementation
and it is short in time which makes it viable for industrialized high volume integrated
IC production test.
Second, a BIST model intended for the characterization of I/Q offset, gain and
phase mismatch of IQ transmitters without relying on external equipment is intro-
duced. The proposed BIST method is based on on-chip amplitude measurement as
in prior works however,here the variations in the BIST circuit do not affect the target
parameter estimation accuracy since measurements are designed to be relative. The
BIST circuit is implemented in 130nm technology and can be used for post-production
and in-field calibration.
Third, a programmable low noise amplifier (LNA) is proposed which is adaptable
to different application scenarios depending on the specification requirements. Its
performance is optimized with regards to required specifications e.g. distance, power
consumption, BER, data rate, etc.The statistical modeling is used to capture the
correlations among measured performance parameters and calibration modes for fast
adaptation. Machine learning technique is used to capture these non-linear correlations and build the probability distribution of a target parameter based on measurement results of the correlated parameters. The proposed concept is demonstrated by
embedding built-in tuning knobs in LNA design in 130nm technology. The tuning
knobs are carefully designed to provide independent combinations of important per-
formance parameters such as gain and linearity. Minimum number of switches are
used to provide the desired tuning range without a need for an external analog input.
ContributorsShafiee, Maryam (Author) / Ozev, Sule (Thesis advisor) / Diaz, Rodolfo (Committee member) / Ogras, Umit Y. (Committee member) / Bakkaloglu, Bertan (Committee member) / Arizona State University (Publisher)
Created2018
Description
Programmable metallization cell (PMC) technology employs the mechanisms of metal ion transport in solid electrolytes (SE) and electrochemical redox reactions in order to form metallic electrodeposits. When a positive bias is applied to an anode opposite to a cathode, atoms at the anode are oxidized to ions and dissolve into the SE. Under the influence of the electric field, the ions move to the cathode and become reduced to form the electrodeposits. These electrodeposits are filamentary in nature and persistent, and since they are metallic can alter the physical characteristics of the material on which they are formed. PMCs can be used as next generation memories, radio frequency (RF) switches and physical unclonable functions (PUFs).
The morphology of the filaments is impacted by the biasing conditions. Under a relatively high applied electric field, they form as dendritic elements with a low fractal dimension (FD), whereas a low electric field leads to high FD features. Ion depletion effects in the SE due to low ion diffusivity/mobility also influences the morphology by limiting the ion supply into the growing electrodeposit.
Ion transport in SE is due to hopping transitions driven by drift and diffusion force. A physical model of ion hopping with Brownian motion has been proposed, in which the ion transitions are random when time window is larger than characteristic time. The random growth process of filaments in PMC adds entropy to the electrodeposition, which leads to random features in the dendritic patterns. Such patterns has extremely high information capacity due to the fractal nature of the electrodeposits.
In this project, lateral-growth PMCs were fabricated, whose LRS resistance is less than 10Ω, which can be used as RF switches. Also, an array of radial-growth PMCs was fabricated, on which multiple dendrites, all with different shapes, could be grown simultaneously. Those patterns can be used as secure keys in PUFs and authentication can be performed by optical scanning.
A kinetic Monte Carlo (KMC) model is developed to simulate the ion transportation in SE under electric field. The simulation results matched experimental data well that validated the ion hopping model.
The morphology of the filaments is impacted by the biasing conditions. Under a relatively high applied electric field, they form as dendritic elements with a low fractal dimension (FD), whereas a low electric field leads to high FD features. Ion depletion effects in the SE due to low ion diffusivity/mobility also influences the morphology by limiting the ion supply into the growing electrodeposit.
Ion transport in SE is due to hopping transitions driven by drift and diffusion force. A physical model of ion hopping with Brownian motion has been proposed, in which the ion transitions are random when time window is larger than characteristic time. The random growth process of filaments in PMC adds entropy to the electrodeposition, which leads to random features in the dendritic patterns. Such patterns has extremely high information capacity due to the fractal nature of the electrodeposits.
In this project, lateral-growth PMCs were fabricated, whose LRS resistance is less than 10Ω, which can be used as RF switches. Also, an array of radial-growth PMCs was fabricated, on which multiple dendrites, all with different shapes, could be grown simultaneously. Those patterns can be used as secure keys in PUFs and authentication can be performed by optical scanning.
A kinetic Monte Carlo (KMC) model is developed to simulate the ion transportation in SE under electric field. The simulation results matched experimental data well that validated the ion hopping model.
ContributorsYu, Weijie (Author) / Kozicki, Michael N (Thesis advisor) / Barnaby, Hugh (Thesis advisor) / Diaz, Rodolfo (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2015
Description
We present fast and robust numerical algorithms for 3-D scattering from perfectly electrical conducting (PEC) and dielectric random rough surfaces in microwave remote sensing. The Coifman wavelets or Coiflets are employed to implement Galerkin’s procedure in the method of moments (MoM). Due to the high-precision one-point quadrature, the Coiflets yield fast evaluations of the most off-diagonal entries, reducing the matrix fill effort from O(N^2) to O(N). The orthogonality and Riesz basis of the Coiflets generate well conditioned impedance matrix, with rapid convergence for the conjugate gradient solver. The resulting impedance matrix is further sparsified by the matrix-formed standard fast wavelet transform (SFWT). By properly selecting multiresolution levels of the total transformation matrix, the solution precision can be enhanced while matrix sparsity and memory consumption have not been noticeably sacrificed. The unified fast scattering algorithm for dielectric random rough surfaces can asymptotically reduce to the PEC case when the loss tangent grows extremely large. Numerical results demonstrate that the reduced PEC model does not suffer from ill-posed problems. Compared with previous publications and laboratory measurements, good agreement is observed.
ContributorsZhang, Lisha (Author) / Pan, George (Thesis advisor) / Diaz, Rodolfo (Committee member) / Aberle, James T., 1961- (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Wide spread adoption of photovoltaic technology is limited by cost. Developing photovoltaics based on low-cost materials and processing techniques is one strategy for reducing the cost of electricity generated by photovoltaics. With this in mind, novel porphyrin and porphyrin-fullerene electropolymers have been developed here at Arizona State University. Porphyrins are attractive for inclusion in the light absorbing layer of photovoltaics due to their high absorption coefficients (on the order of 105 cm-1) and porphyrin-fullerene dyads are attractive for use in photovoltaics due to their ability to produce ultrafast photoinduced charge separation (on the order of 10-15 s). The focus of this thesis is the characterization of the photovoltaic properties of these electropolymer films. Films formed on transparent conductive oxide (TCO) substrates were contacted using a mercury drop electrode in order to measure photocurrent spectra and current-voltage curves. Surface treatment of both the TCO substrate and the mercury drop is shown to have a dramatic effect on the photovoltaic performance of the electropolymer films. Treating the TCO substrates with chlorotrimethylsilane and the mercury drop with hexanethiol was found to produce an optimal tradeoff between photocurrent and photovoltage. Incident photon to current efficiency spectra of the films show that the dominant photocurrent generation mechanism in this system is located at the polymer-mercury interface. The optical field intensity at this interface approaches zero due to interference from the light reflected by the mercury surface. Reliance upon photocurrent generation at this interface limits the performance of this system and suggests that these polymers may be useful in solar cells which have structures optimized to take advantage of their internal optical field distributions.
ContributorsBridgewater, James W (Author) / Gust, Devens (Thesis advisor) / Tao, Nongjian (Thesis advisor) / Gould, Ian (Committee member) / Diaz, Rodolfo (Committee member) / Arizona State University (Publisher)
Created2014
Description
Parkinson's disease is a neurodegenerative condition diagnosed on patients with
clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated
number of patients living with Parkinson's disease around the world is seven
to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor
signs of Parkinson's disease patients. It is an advanced surgical technique that is used
when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation.
This work proposes a behavior recognition model for patients with Parkinson's
disease. In particular, an adaptive learning method is proposed to classify behavioral
tasks of Parkinson's disease patients using local field potential and electrocorticography
signals that are collected during DBS implantation surgeries. Unique patterns
exhibited between these signals in a matched feature space would lead to distinction
between motor and language behavioral tasks. Unique features are first extracted
from deep brain signals in the time-frequency space using the matching pursuit decomposition
algorithm. The Dirichlet process Gaussian mixture model uses the extracted
features to cluster the different behavioral signal patterns, without training or
any prior information. The performance of the method is then compared with other
machine learning methods and the advantages of each method is discussed under
different conditions.
clinical history and motor signs of tremor, rigidity and bradykinesia, and the estimated
number of patients living with Parkinson's disease around the world is seven
to ten million. Deep brain stimulation (DBS) provides substantial relief of the motor
signs of Parkinson's disease patients. It is an advanced surgical technique that is used
when drug therapy is no longer sufficient for Parkinson's disease patients. DBS alleviates the motor symptoms of Parkinson's disease by targeting the subthalamic nucleus using high-frequency electrical stimulation.
This work proposes a behavior recognition model for patients with Parkinson's
disease. In particular, an adaptive learning method is proposed to classify behavioral
tasks of Parkinson's disease patients using local field potential and electrocorticography
signals that are collected during DBS implantation surgeries. Unique patterns
exhibited between these signals in a matched feature space would lead to distinction
between motor and language behavioral tasks. Unique features are first extracted
from deep brain signals in the time-frequency space using the matching pursuit decomposition
algorithm. The Dirichlet process Gaussian mixture model uses the extracted
features to cluster the different behavioral signal patterns, without training or
any prior information. The performance of the method is then compared with other
machine learning methods and the advantages of each method is discussed under
different conditions.
ContributorsDutta, Arindam (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Holbert, Keith E. (Committee member) / Bliss, Daniel W. (Committee member) / Arizona State University (Publisher)
Created2015
Description
RF convergence of radar and communications users is rapidly becoming an issue for a multitude of stakeholders. To hedge against growing spectral congestion, research into cooperative radar and communications systems has been identified as a critical necessity for the United States and other countries. Further, the joint sensing-communicating paradigm appears imminent in several technological domains. In the pursuit of co-designing radar and communications systems that work cooperatively and benefit from each other's existence, joint radar-communications metrics are defined and bounded as a measure of performance. Estimation rate is introduced, a novel measure of radar estimation information as a function of time. Complementary to communications data rate, the two systems can now be compared on the same scale. An information-centric approach has a number of advantages, defining precisely what is gained through radar illumination and serves as a measure of spectral efficiency. Bounding radar estimation rate and communications data rate jointly, systems can be designed as a joint optimization problem.
ContributorsPaul, Bryan (Author) / Bliss, Daniel W. (Thesis advisor) / Berisha, Visar (Committee member) / Kosut, Oliver (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2017
Description
Digital imaging and image processing technologies have revolutionized the way in which
we capture, store, receive, view, utilize, and share images. In image-based applications,
through different processing stages (e.g., acquisition, compression, and transmission), images
are subjected to different types of distortions which degrade their visual quality. Image
Quality Assessment (IQA) attempts to use computational models to automatically evaluate
and estimate the image quality in accordance with subjective evaluations. Moreover, with
the fast development of computer vision techniques, it is important in practice to extract
and understand the information contained in blurred images or regions.
The work in this dissertation focuses on reduced-reference visual quality assessment of
images and textures, as well as perceptual-based spatially-varying blur detection.
A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The
proposed method requires a very small number of reduced-reference (RR) features. Extensive
experiments performed on different benchmark databases demonstrate that the proposed
RRIQA method, delivers highly competitive performance as compared with the
state-of-the-art RRIQA models for both natural and texture images.
In the context of texture, the effect of texture granularity on the quality of synthesized
textures is studied. Moreover, two RR objective visual quality assessment methods that
quantify the perceived quality of synthesized textures are proposed. Performance evaluations
on two synthesized texture databases demonstrate that the proposed RR metrics outperforms
full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in
predicting the perceived visual quality of the synthesized textures.
Last but not least, an effective approach to address the spatially-varying blur detection
problem from a single image without requiring any knowledge about the blur type, level,
or camera settings is proposed. The evaluations of the proposed approach on a diverse
sets of blurry images with different blur types, levels, and content demonstrate that the
proposed algorithm performs favorably against the state-of-the-art methods qualitatively
and quantitatively.
we capture, store, receive, view, utilize, and share images. In image-based applications,
through different processing stages (e.g., acquisition, compression, and transmission), images
are subjected to different types of distortions which degrade their visual quality. Image
Quality Assessment (IQA) attempts to use computational models to automatically evaluate
and estimate the image quality in accordance with subjective evaluations. Moreover, with
the fast development of computer vision techniques, it is important in practice to extract
and understand the information contained in blurred images or regions.
The work in this dissertation focuses on reduced-reference visual quality assessment of
images and textures, as well as perceptual-based spatially-varying blur detection.
A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The
proposed method requires a very small number of reduced-reference (RR) features. Extensive
experiments performed on different benchmark databases demonstrate that the proposed
RRIQA method, delivers highly competitive performance as compared with the
state-of-the-art RRIQA models for both natural and texture images.
In the context of texture, the effect of texture granularity on the quality of synthesized
textures is studied. Moreover, two RR objective visual quality assessment methods that
quantify the perceived quality of synthesized textures are proposed. Performance evaluations
on two synthesized texture databases demonstrate that the proposed RR metrics outperforms
full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in
predicting the perceived visual quality of the synthesized textures.
Last but not least, an effective approach to address the spatially-varying blur detection
problem from a single image without requiring any knowledge about the blur type, level,
or camera settings is proposed. The evaluations of the proposed approach on a diverse
sets of blurry images with different blur types, levels, and content demonstrate that the
proposed algorithm performs favorably against the state-of-the-art methods qualitatively
and quantitatively.
ContributorsGolestaneh, Seyedalireza (Author) / Karam, Lina (Thesis advisor) / Bliss, Daniel W. (Committee member) / Li, Baoxin (Committee member) / Turaga, Pavan K. (Committee member) / Arizona State University (Publisher)
Created2018