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The first half of this dissertation introduces a minimum cost incentive mechanism for collecting discrete distributed private data for big-data analysis. The goal of an incentive mechanism is to incentivize informative reports and make sure randomization in the reported data does not exceed a target level. It answers two fundamental questions: what is the minimum payment required to incentivize an individual to submit data with quality level $\epsilon$? and what incentive mechanisms can achieve the minimum payment? A lower bound on the minimum amount of payment required for guaranteeing quality level $\epsilon$ is derived. Inspired by the lower bound, our incentive mechanism (WINTALL) first decides a winning answer based on reported data, then pays to individuals whose reported data match the winning answer. The expected payment of WINTALL matches lower bound asymptotically. Real-world experiments on Amazon Mechanical Turk are presented to further illustrate novelty of the principle behind WINTALL.
The second half studies problem of iterative training in Federated Learning. A system with a single parameter server and $M$ client devices is considered for training a predictive learning model with distributed data. The clients communicate with the parameter server using a common wireless channel so each time, only one device can transmit. The training is an iterative process consisting of multiple rounds. Adaptive training is considered where the parameter server decides when to stop/restart a new round, so the problem is formulated as an optimal stopping problem. While this optimal stopping problem is difficult to solve, a modified optimal stopping problem is proposed. Then a low complexity algorithm is introduced to solve the modified problem, which also works for the original problem. Experiments on a real data set shows significant improvements compared with policies collecting a fixed number of updates in each iteration.
The second half studies problem of iterative training in Federated Learning. A system with a single parameter server and $M$ client devices is considered for training a predictive learning model with distributed data. The clients communicate with the parameter server using a common wireless channel so each time, only one device can transmit. The training is an iterative process consisting of multiple rounds. Adaptive training is considered where the parameter server decides when to stop/restart a new round, so the problem is formulated as an optimal stopping problem. While this optimal stopping problem is difficult to solve, a modified optimal stopping problem is proposed. Then a low complexity algorithm is introduced to solve the modified problem, which also works for the original problem. Experiments on a real data set shows significant improvements compared with policies collecting a fixed number of updates in each iteration.
ContributorsJiang, Pengfei (Author) / Ying, Lei (Thesis advisor) / Zhang, Junshan (Committee member) / Zhang, Yanchao (Committee member) / Wang, Weina (Committee member) / Arizona State University (Publisher)
Created2020
Description
In the era of artificial intelligent (AI), deep neural networks (DNN) have achieved accuracy on par with humans on a variety of recognition tasks. However, the high computation and storage requirement of DNN training and inference have posed challenges to deploying or locally training the DNNs on mobile and wearable devices. Energy-efficient hardware innovation from circuit to architecture level is required.In this dissertation, a smart electrocardiogram (ECG) processor is first presented for ECG-based authentication as well as cardiac monitoring. The 65nm testchip consumes 1.06 μW at 0.55 V for real-time ECG authentication achieving equal error rate of 1.7% for authentication on an in-house 645-subject database. Next, a couple of SRAM-based in-memory computing (IMC) accelerators for deep learning algorithms are presented. Two single-array macros titled XNOR-SRAM and C3SRAM are based on resistive and capacitive networks for XNOR-ACcumulation (XAC) operations, respectively. XNOR-SRAM and C3SRAM macros in 65nm CMOS achieve energy efficiency of 403 TOPS/W and 672 TOPS/W, respectively. Built on top of these two single-array macro designs, two multi-array architectures are presented. The XNOR-SRAM based architecture titled “Vesti” is designed to support configurable multibit activations and large-scale DNNs seamlessly. Vesti employs double-buffering with two groups of in-memory computing SRAMs, effectively hiding the write latency of IMC SRAMs. The Vesti accelerator in 65nm CMOS achieves energy consumption of <20 nJ for MNIST classification and <40μJ for CIFAR-10 classification at 1.0 V supply. More recently, a programmable IMC accelerator (PIMCA) integrating 108 C3SRAM macros of a total size of 3.4 Mb is proposed. The28nm prototype chip achieves system-level energy efficiency of 437/62 TOPS/W at 40 MHz, 1 V supply for DNNs with 1b/2b precision.
In addition to the IMC works, this dissertation also presents a convolutional neural network (CNN) learning processor, which accelerates the stochastic gradient descent (SGD) with momentum based training algorithm in 16-bit fixed-point precision. The65nm CNN learning processor achieves peak energy efficiency of 2.6 TOPS/W for16-bit fixed-point operations, consuming 10.45 mW at 0.55 V. In summary, in this dissertation, several hardware innovations from circuit to architecture level are presented, exploiting the reduced algorithm complexity with pruning and low-precision quantization techniques. In particular, macro-level and system-level SRAM based IMC works presented in this dissertation show that SRAM based IMC is one of the promising solutions for energy-efficient intelligent systems.
In addition to the IMC works, this dissertation also presents a convolutional neural network (CNN) learning processor, which accelerates the stochastic gradient descent (SGD) with momentum based training algorithm in 16-bit fixed-point precision. The65nm CNN learning processor achieves peak energy efficiency of 2.6 TOPS/W for16-bit fixed-point operations, consuming 10.45 mW at 0.55 V. In summary, in this dissertation, several hardware innovations from circuit to architecture level are presented, exploiting the reduced algorithm complexity with pruning and low-precision quantization techniques. In particular, macro-level and system-level SRAM based IMC works presented in this dissertation show that SRAM based IMC is one of the promising solutions for energy-efficient intelligent systems.
ContributorsYin, Shihui (Author) / Seo, Jae-sun Seo J. S. (Thesis advisor) / Cao, Yu Y. C. (Committee member) / Vrudhula, Sarma S. V. (Committee member) / Chakrabarti, Chaitali C. C. (Committee member) / Arizona State University (Publisher)
Created2020
Description
Unmanned aerial systems (UASs) have recently enabled novel applications such as passenger transport and package delivery, but are increasingly vulnerable to cyberattack and therefore difficult to certify. Legacy systems such as GPS provide these capabilities extremely well, but are sensitive to spoofing and hijacking. An alternative intelligent transport system (ITS) was developed that provides highly secure communications, positioning, and timing synchronization services to networks of cooperative RF users, termed Communications and High-Precision Positioning (CHP2) system. This technology was implemented on consumer-off-the-shelf (COTS) hardware and it offers rapid (<100 ms) and precise (<5 cm) positioning capabilities in over-the-air experiments using flexible ground stations and UAS platforms using limited bandwidth (10 MHz). In this study, CHP2 is considered in the context of safety-critical and resource limited transport applications and urban air mobility. The two-way ranging (TWR) protocol over a joint positioning-communications waveform enables distributed coherence and time-of-flight(ToF) estimation. In a multi-antenna setup, the cross-platform ranging on participating nodes in the network translate to precise target location and orientation. In the current form, CHP2 necessitates a cooperative timing exchange at regular intervals. Dynamic resource management supports higher user densities by constantly renegotiating spectral access depending on need and opportunity. With these novel contributions to the field of integrated positioning and communications, CHP2 is a suitable candidate to provide both communications, navigation, and surveillance (CNS) and alternative positioning, navigation, and timing (APNT) services for high density safety-critical transport applications on a variety of vehicular platforms.
ContributorsSrinivas, Sharanya (Author) / Bliss, Daniel W. (Thesis advisor) / Richmond, Christ D. (Committee member) / Chakrabarti, Chaitali (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2020
Description
Wearable technology has brought in a rapid shift in the areas of healthcare and lifestyle management. The recent development and usage of wearable devices like smart watches has created significant impact in areas like fitness management, exercise tracking, sleep quality assessment and early diagnosis of diseases like asthma, sleep apnea etc. This thesis is dedicated to the development of wearable systems and algorithms to fulfill unmet needs in the area of cardiorespiratory monitoring.
First, a pneumotach based flow sensing technique has been developed and integrated into a face mask for respiratory profile tracking. Algorithms have been developed to convert the pressure profile into respiratory flow rate profile. Gyroscope-based correction is used to remove motion artifacts that arise from daily activities. By using Principal Component Analysis, the follow-up work established a unique respiratory signature for each subject based on the flow profile and lung parameters computed using the wearable mask system.
Next, wristwatch devices to track transcutaneous gases like oxygen (TcO2) and carbon dioxide (TcCO2), and oximetry (SpO2) have been developed. Two chemical sensing approaches have been explored. In the first approach, miniaturized low-cost commercial sensors have been integrated into the wristwatch for transcutaneous gas sensing. In the second approach, CMOS camera-based colorimetric sensors are integrated into the wristwatch, where a part of camera frame is used for photoplethysmography while the remaining part tracks the optical signal from colorimetric sensors.
Finally, the wireless connectivity using Bluetooth Low Energy (BLE) in wearable systems has been explored and a data transmission protocol between wearables and host for reliable transfer has been developed. To improve the transmission reliability, the host is designed to use queue-based re-request routine to notify the wearable device of the missing packets that should be re-transmitted. This approach avoids the issue of host dependent packet losses and ensures that all the necessary information is received.
The works in this thesis have provided technical solutions to address challenges in wearable technologies, ranging from chemical sensing, flow sensing, data analysis, to wireless data transmission. These works have demonstrated transformation of traditional bench-top medical equipment into non-invasive, unobtrusive, ergonomic & stand-alone healthcare devices.
First, a pneumotach based flow sensing technique has been developed and integrated into a face mask for respiratory profile tracking. Algorithms have been developed to convert the pressure profile into respiratory flow rate profile. Gyroscope-based correction is used to remove motion artifacts that arise from daily activities. By using Principal Component Analysis, the follow-up work established a unique respiratory signature for each subject based on the flow profile and lung parameters computed using the wearable mask system.
Next, wristwatch devices to track transcutaneous gases like oxygen (TcO2) and carbon dioxide (TcCO2), and oximetry (SpO2) have been developed. Two chemical sensing approaches have been explored. In the first approach, miniaturized low-cost commercial sensors have been integrated into the wristwatch for transcutaneous gas sensing. In the second approach, CMOS camera-based colorimetric sensors are integrated into the wristwatch, where a part of camera frame is used for photoplethysmography while the remaining part tracks the optical signal from colorimetric sensors.
Finally, the wireless connectivity using Bluetooth Low Energy (BLE) in wearable systems has been explored and a data transmission protocol between wearables and host for reliable transfer has been developed. To improve the transmission reliability, the host is designed to use queue-based re-request routine to notify the wearable device of the missing packets that should be re-transmitted. This approach avoids the issue of host dependent packet losses and ensures that all the necessary information is received.
The works in this thesis have provided technical solutions to address challenges in wearable technologies, ranging from chemical sensing, flow sensing, data analysis, to wireless data transmission. These works have demonstrated transformation of traditional bench-top medical equipment into non-invasive, unobtrusive, ergonomic & stand-alone healthcare devices.
ContributorsTipparaju, Vishal Varun (Author) / Xian, Xiaojun (Thesis advisor) / Forzani, Erica (Thesis advisor) / Blain Christen, Jennifer (Committee member) / Angadi, Siddhartha (Committee member) / Arizona State University (Publisher)
Created2020
Description
The rationale of this thesis is to provide a thorough understanding of spalling for semiconductor materials and develop a low temperature spalling technology that reduces the surface roughness of the spalled wafers for Photovoltaics applications.
ContributorsGuimera Coll, Pablo (Author) / Bertoni, Mariana I (Thesis advisor) / Meier, Rico (Committee member) / Holman, Zachary (Committee member) / Wang, Qing Hua (Committee member) / Arizona State University (Publisher)
Created2020
Description
An ongoing effort in the photovoltaic (PV) industry is to reduce the major manufacturing cost components of solar cells, the great majority of which are based on crystalline silicon (c-Si). This includes the substitution of screenprinted silver (Ag) cell contacts with alternative copper (Cu)-based contacts, usually applied with plating. Plated Cu contact schemes have been under study for many years with only minor traction in industrial production. One of the more commonly-cited barriers to the adoption of Cu-based contacts for photovoltaics is long-term reliability, as Cu is a significant contaminant in c-Si, forming precipitates that degrade performance via degradation of diode character and reduction of minority carrier lifetime. Cu contamination from contacts might cause degradation during field deployment if Cu is able to ingress into c-Si. Furthermore, Cu contamination is also known to cause a form of light-induced degradation (LID) which further degrades carrier lifetime when cells are exposed to light.
Prior literature on Cu-contact reliability tended to focus on accelerated testing at the cell and wafer level that may not be entirely replicative of real-world environmental stresses in PV modules. This thesis is aimed at advancing the understanding of Cu-contact reliability from the perspective of quasi-commercial modules under more realistic stresses. In this thesis, c-Si solar cells with Cu-plated contacts are fabricated, made into PV modules, and subjected to environmental stress in an attempt to induce hypothesized failure modes and understand any new vulnerabilities that Cu contacts might introduce. In particular, damp heat stress is applied to conventional, p-type c-Si modules and high efficiency, n-type c-Si heterojunction modules. I present evidence of Cu-induced diode degradation that also depends on PV module materials, as well as degradation unrelated to Cu, and in either case suggest engineering solutions to the observed degradation. In a forensic search for degradation mechanisms, I present novel evidence of Cu outdiffusion from contact layers and encapsulant-driven contact corrosion as potential key factors. Finally, outdoor exposures to light uncover peculiarities in Cu-plated samples, but do not point to especially serious vulnerabilities.
Prior literature on Cu-contact reliability tended to focus on accelerated testing at the cell and wafer level that may not be entirely replicative of real-world environmental stresses in PV modules. This thesis is aimed at advancing the understanding of Cu-contact reliability from the perspective of quasi-commercial modules under more realistic stresses. In this thesis, c-Si solar cells with Cu-plated contacts are fabricated, made into PV modules, and subjected to environmental stress in an attempt to induce hypothesized failure modes and understand any new vulnerabilities that Cu contacts might introduce. In particular, damp heat stress is applied to conventional, p-type c-Si modules and high efficiency, n-type c-Si heterojunction modules. I present evidence of Cu-induced diode degradation that also depends on PV module materials, as well as degradation unrelated to Cu, and in either case suggest engineering solutions to the observed degradation. In a forensic search for degradation mechanisms, I present novel evidence of Cu outdiffusion from contact layers and encapsulant-driven contact corrosion as potential key factors. Finally, outdoor exposures to light uncover peculiarities in Cu-plated samples, but do not point to especially serious vulnerabilities.
ContributorsKaras, Joseph (Author) / Bowden, Stuart (Thesis advisor) / Alford, Terry (Thesis advisor) / Tamizhmani, Govindasamy (Committee member) / Michaelson, Lynne (Committee member) / Arizona State University (Publisher)
Created2020
Description
There are increasing demands for gas sensors in air quality and human health monitoring applications. The qualifying sensor technology must be highly sensitive towards ppb level gases of interest, such as acetylene (C2H2), hydrogen sulfide (H2S), and volatile organic compounds. Among the commercially available sensor technologies, conductometric gas sensors with nanoparticles of oxide semiconductors as sensing materials hold significant advantages in cost, size, and cross-compatibility. However, semiconductor gas sensors must overcome some major challenges in thermal stability, sensitivity, humidity interference, and selectivity before potential widespread adoption in air quality and human health monitoring applications.
The focus of this dissertation is to tackle these issues by optimizing the composition and the morphology of the nanoparticles, and by innovating the structure of the sensing film assembled with the nanoparticles. From the nanoparticles perspective, the thermal stability of tin oxide nanoparticles with different Al dopant concentrations was studied, and the results indicate that within certain range of doping concentration, the dopants segregated at the grain surface can improve the thermal stability by stabilizing the grain boundaries.
From the sensing film perspective, a novel self-assembly approach was developed for copper oxide nanosheets and the sensor response towards H2S gas was revealed to decrease monotonically by more than 60% as the number of layers increase from 1 to 300 (thickness: 0.03-10 μm). Moreover, a sensing mechanism study on the humidity influence on H2S detection was performed to gain more understandings of the role of the hydroxyl group in the surface reaction, and humidity independent response was observed in the monolayer film at 325 ℃. With a more precise deposition tool (Langmuir-Blodgett trough), monolayer film of zinc oxide nanowires sensitized with gold catalyst was prepared, and highly sensitive and specific response to C2H2 in the ppb range was observed. Furthermore, the effect of surface topography of the monolayer film on stabilizing noble metal catalyst, and the sensitization mechanism of gold were investigated.
Lastly, a semiconductor sensor array was developed to analyze the composition of gases dissolved in transformer oil to demonstrate the industrial application of this sensor technology.
The focus of this dissertation is to tackle these issues by optimizing the composition and the morphology of the nanoparticles, and by innovating the structure of the sensing film assembled with the nanoparticles. From the nanoparticles perspective, the thermal stability of tin oxide nanoparticles with different Al dopant concentrations was studied, and the results indicate that within certain range of doping concentration, the dopants segregated at the grain surface can improve the thermal stability by stabilizing the grain boundaries.
From the sensing film perspective, a novel self-assembly approach was developed for copper oxide nanosheets and the sensor response towards H2S gas was revealed to decrease monotonically by more than 60% as the number of layers increase from 1 to 300 (thickness: 0.03-10 μm). Moreover, a sensing mechanism study on the humidity influence on H2S detection was performed to gain more understandings of the role of the hydroxyl group in the surface reaction, and humidity independent response was observed in the monolayer film at 325 ℃. With a more precise deposition tool (Langmuir-Blodgett trough), monolayer film of zinc oxide nanowires sensitized with gold catalyst was prepared, and highly sensitive and specific response to C2H2 in the ppb range was observed. Furthermore, the effect of surface topography of the monolayer film on stabilizing noble metal catalyst, and the sensitization mechanism of gold were investigated.
Lastly, a semiconductor sensor array was developed to analyze the composition of gases dissolved in transformer oil to demonstrate the industrial application of this sensor technology.
ContributorsMiao, Jiansong (Author) / Lin, Jerry Y.S. (Thesis advisor) / Forzani, Erica (Committee member) / Liu, Jingyue (Committee member) / Li, Jian (Committee member) / Arizona State University (Publisher)
Created2020
Description
The availability of data for monitoring and controlling the electrical grid has increased exponentially over the years in both resolution and quantity leaving a large data footprint. This dissertation is motivated by the need for equivalent representations of grid data in lower-dimensional feature spaces so that machine learning algorithms can be employed for a variety of purposes. To achieve that, without sacrificing the interpretation of the results, the dissertation leverages the physics behind power systems, well-known laws that underlie this man-made infrastructure, and the nature of the underlying stochastic phenomena that define the system operating conditions as the backbone for modeling data from the grid.
The first part of the dissertation introduces a new framework of graph signal processing (GSP) for the power grid, Grid-GSP, and applies it to voltage phasor measurements that characterize the overall system state of the power grid. Concepts from GSP are used in conjunction with known power system models in order to highlight the low-dimensional structure in data and present generative models for voltage phasors measurements. Applications such as identification of graphical communities, network inference, interpolation of missing data, detection of false data injection attacks and data compression are explored wherein Grid-GSP based generative models are used.
The second part of the dissertation develops a model for a joint statistical description of solar photo-voltaic (PV) power and the outdoor temperature which can lead to better management of power generation resources so that electricity demand such as air conditioning and supply from solar power are always matched in the face of stochasticity. The low-rank structure inherent in solar PV power data is used for forecasting and to detect partial-shading type of faults in solar panels.
The first part of the dissertation introduces a new framework of graph signal processing (GSP) for the power grid, Grid-GSP, and applies it to voltage phasor measurements that characterize the overall system state of the power grid. Concepts from GSP are used in conjunction with known power system models in order to highlight the low-dimensional structure in data and present generative models for voltage phasors measurements. Applications such as identification of graphical communities, network inference, interpolation of missing data, detection of false data injection attacks and data compression are explored wherein Grid-GSP based generative models are used.
The second part of the dissertation develops a model for a joint statistical description of solar photo-voltaic (PV) power and the outdoor temperature which can lead to better management of power generation resources so that electricity demand such as air conditioning and supply from solar power are always matched in the face of stochasticity. The low-rank structure inherent in solar PV power data is used for forecasting and to detect partial-shading type of faults in solar panels.
ContributorsRamakrishna, Raksha (Author) / Scaglione, Anna (Thesis advisor) / Cochran, Douglas (Committee member) / Spanias, Andreas (Committee member) / Vittal, Vijay (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2020
Description
Semantic image segmentation has been a key topic in applications involving image processing and computer vision. Owing to the success and continuous research in the field of deep learning, there have been plenty of deep learning-based segmentation architectures that have been designed for various tasks. In this thesis, deep-learning architectures for a specific application in material science; namely the segmentation process for the non-destructive study of the microstructure of Aluminum Alloy AA 7075 have been developed. This process requires the use of various imaging tools and methodologies to obtain the ground-truth information. The image dataset obtained using Transmission X-ray microscopy (TXM) consists of raw 2D image specimens captured from the projections at every beam scan. The segmented 2D ground-truth images are obtained by applying reconstruction and filtering algorithms before using a scientific visualization tool for segmentation. These images represent the corrosive behavior caused by the precipitates and inclusions particles on the Aluminum AA 7075 alloy. The study of the tools that work best for X-ray microscopy-based imaging is still in its early stages.
In this thesis, the underlying concepts behind Convolutional Neural Networks (CNNs) and state-of-the-art Semantic Segmentation architectures have been discussed in detail. The data generation and pre-processing process applied to the AA 7075 Data have also been described, along with the experimentation methodologies performed on the baseline and four other state-of-the-art Segmentation architectures that predict the segmented boundaries from the raw 2D images. A performance analysis based on various factors to decide the best techniques and tools to apply Semantic image segmentation for X-ray microscopy-based imaging was also conducted.
In this thesis, the underlying concepts behind Convolutional Neural Networks (CNNs) and state-of-the-art Semantic Segmentation architectures have been discussed in detail. The data generation and pre-processing process applied to the AA 7075 Data have also been described, along with the experimentation methodologies performed on the baseline and four other state-of-the-art Segmentation architectures that predict the segmented boundaries from the raw 2D images. A performance analysis based on various factors to decide the best techniques and tools to apply Semantic image segmentation for X-ray microscopy-based imaging was also conducted.
ContributorsBarboza, Daniel (Author) / Turaga, Pavan (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2020
Description
Perovskite solar cells are the next generation organic-inorganic hybrid technology and have achieved remarkable efficiencies comparable to Si-based conventional solar cells. Since their inception in 2009 with an efficiency of 3.9%, they have improved tremendously over the past decade and recently demonstrated 25.2% efficiency for single-junction devices. There are a few hurdles, however, that prevent this technology from realizing their full potential, such as stability and toxicity of the perovskites. Apart from solution processing in the fabrication of perovskites, precursor composition plays a major role in determining the quality of the thin film and its general properties. This work studies novel approaches for improving the efficiency and stability of the perovskite solar cells with minimized toxicity. The effect of excess Pb on photo-degradation in MAPbI3 perovskites in an inverted device architecture was studied with a focus on improving stability and efficiency. Precursor concentration with 5% excess Pb was found to be optimal for better efficiency and stability against photo-degradation. Further improvements in efficiency were made possible through the addition of Zirconium Acetylacetonate as a secondary electron buffer layer. A concentration of 1.5mg/ml was found to be optimal for demonstrating better efficiency and stability. Partial substitution of Pb with non-toxic Sr was also studied for improving the stability of inverted devices. Using acetate-derived precursors, 10% Sr was introduced into perovskites for improvements to the stability of the device.
In another study, triple-cation perovskites with FAMACs cations were studied with doping different amounts of Phenyl Ethyl Ammonium (PEA) to induce a quasi 2D-3D structure for improved moisture stability. Doping the perovskite with 1.67% PEA was found to be best for improved morphology with fewer pinholes, which further resulted in better VOC and stability. A passivation effect for triple-cation perovskites was further proposed with the addition of a Guanidinium Iodide layer on the perovskite. Concentrations of 1mg/ml and 2mg/ml were demonstrated to be best for reducing defects and trap states and increasing the overall stability of the device.
In another study, triple-cation perovskites with FAMACs cations were studied with doping different amounts of Phenyl Ethyl Ammonium (PEA) to induce a quasi 2D-3D structure for improved moisture stability. Doping the perovskite with 1.67% PEA was found to be best for improved morphology with fewer pinholes, which further resulted in better VOC and stability. A passivation effect for triple-cation perovskites was further proposed with the addition of a Guanidinium Iodide layer on the perovskite. Concentrations of 1mg/ml and 2mg/ml were demonstrated to be best for reducing defects and trap states and increasing the overall stability of the device.
ContributorsYerramilli, Aditya (Author) / Alford, Terry (Thesis advisor) / Theodore, David (Committee member) / Chen, Yuanqing (Committee member) / Arizona State University (Publisher)
Created2020