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Description
Respiratory behavior provides effective information to characterize lung functionality, including respiratory rate, respiratory profile, and respiratory volume. Current methods have limited capabilities of continuous characterization of respiratory behavior and are primarily targeting the measurement of respiratory rate, which has relatively less value in clinical application. In this dissertation, a wireless wearable sensor on a paper substrate is developed to continuously characterize respiratory behavior and deliver clinically relevant parameters, contributing to asthma control. Based on the anatomical analysis and experimental results, the optimum site for the wireless wearable sensor is on the midway of the xiphoid process and the costal margin, corresponding to the abdomen-apposed rib cage. At the wearing site, the linear strain change during respiration is measured and converted to lung volume by the wireless wearable sensor utilizing a distance-elapsed ultrasound. An on-board low-power Bluetooth module transmits the temporal lung volume change to a smartphone, where a custom-programmed app computes to show the clinically relevant parameters, such as forced vital capacity (FVC) and forced expiratory volume delivered in the first second (FEV1) and the FEV1/FVC ratio. Enhanced by a simple, yet effective machine-learning algorithm, a system consisting of two wireless wearable sensors accurately extracts respiratory features and classifies the respiratory behavior within four postures among different subjects, demonstrating that the respiratory behaviors are individual- and posture-dependent contributing to monitoring the posture-related respiratory diseases. The continuous and accurate monitoring of respiratory behaviors can track the respiratory disorders and diseases' progression for timely and objective approaches for control and management.
ContributorsChen, Ang (Author) / Cao, Yu (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Allee, David (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2020
Description
Combining the rapid development of semiconductor technologies, miniaturization of integrated circuits (ICs), and scaling down the device size is trending towards faster, cheaper, and more reliable components for low-power integrated circuits. Most research and development relate to efficiency, structure, materials, and performance. However, the thermal problem is also created and becomes more critical with shrinking device dimensions and increased integration densities, such that it affects the device performance and leads to degradation and damage. At the nanometer scale, the self-heating effect (SHE) is one of the main factors to degrade devices. Therefore, tracking and quantifying the SHE is important for reliability and efficiency issues. In this dissertation, engineers design two identical and closely spaced 40nm gate length silicon-on-insulator (SOI) n-channel metal-oxide-semiconductor-field-effect transistors (NMOSFETs) that share a common source with the same active silicon region. One of the MOSFETs acts as a heater to heat-up the active region, while the other one is a thermometer to evaluate the SHE and local temperature changes. The thermometer provides a method to calibrate the numerical models of self-heating and track the heat flow. Moreover, it also involves a trap-rich SOI wafer technology, in which a trap-rich layer, with higher resistivity and lower thermal conductivity compared to conventional bulk silicon substrates. The trap-rich SOI substrates can reduce the cross-talk and minimize the power consumption to increase the system performance. In particular, it offers a solution to radio frequency integrated circuits (RFICs) which require fast switching and low leakage. In high power amplifier (PA) applications, Watt-level PAs operates at less than 50% efficiency because of temperature limitations. The author uses experimental measurements of the local temperature changes, combined with simulations to examine the heat flow and temperature distribution. The approach may be useful to build a self-test application, because it can quantify the temperature changes by putting one or multiple NMOSFET thermometers around a complementary metal-oxide-semiconductor (CMOS) power amplifier, while only adding minimum die area. It points to ways in which it can optimize the reliability of RFIC applications, which operate under high-temperature or high-power conditions to protect the device before it is overheated or damaged.
ContributorsZhang, Xiong (Author) / Thornton, Trevor TT (Thesis advisor) / Vasileska, Dragica DV (Committee member) / Goryll, Michael MG (Committee member) / Myhajlenko, Stefan SM (Committee member) / Arizona State University (Publisher)
Created2020
Description
Traditionally nanoporous gold is created by selective dissolution of silver or copper from a binary silver-gold or copper-gold alloy. These alloys serve as prototypical model systems for a phenomenon referred to as stress-corrosion cracking. Stress-corrosion cracking is the brittle failure of a normally ductile material occurring in a corrosive environment under a tensile stress. Silver-gold can experience this type of brittle fracture for a range of compositions. The corrosion process in this alloy results in a bicontinuous nanoscale morphology composed of gold-rich ligaments and voids often referred to as nanoporous gold. Experiments have shown that monolithic nanoporous gold can sustain high speed cracks which can then be injected into parent-phase alloy. This work compares nanoporous gold created from ordered and disordered copper-gold using digital image analysis and electron backscatter diffraction. Nanoporous gold from both disordered copper-gold and silver-gold, and ordered copper-gold show that grain orientation and shape remain largely unchanged by the dealloying process. Comparing the morphology of the nanoporous gold from ordered and disordered copper-gold with digital image analysis, minimal differences are found between the two and it is concluded that they are not statistically significant. This reveals the robust nature of nanoporous gold morphology against small variations in surface diffusion and parent-phase crystal structure.
Then the corrosion penetration down the grain boundary is compared to the depth of crack injections in polycrystal silver-gold. Based on statistical comparison, the crack-injections penetrate into the parent-phase grain boundary beyond the corrosion-induced porosity. To compare crack injections to stress-corrosion cracking, single crystal silver-gold samples are employed. Due to the cleavage-like nature of the fracture surfaces, electron backscatter diffraction is possible and employed to compare the crystallography of stress-corrosion crack surfaces and crack-injection surfaces. From the crystallographic similarities of these fracture surfaces, it is concluded that stress-corrosion can occur via a series of crack-injection events. This relationship between crack injections and stress corrosion cracking is further examined using electrochemical data from polycrystal silver-gold samples during stress-corrosion cracking. The results support the idea that crack injection is a mechanism for stress-corrosion cracking.
Then the corrosion penetration down the grain boundary is compared to the depth of crack injections in polycrystal silver-gold. Based on statistical comparison, the crack-injections penetrate into the parent-phase grain boundary beyond the corrosion-induced porosity. To compare crack injections to stress-corrosion cracking, single crystal silver-gold samples are employed. Due to the cleavage-like nature of the fracture surfaces, electron backscatter diffraction is possible and employed to compare the crystallography of stress-corrosion crack surfaces and crack-injection surfaces. From the crystallographic similarities of these fracture surfaces, it is concluded that stress-corrosion can occur via a series of crack-injection events. This relationship between crack injections and stress corrosion cracking is further examined using electrochemical data from polycrystal silver-gold samples during stress-corrosion cracking. The results support the idea that crack injection is a mechanism for stress-corrosion cracking.
ContributorsKarasz, Erin (Author) / Sieradzki, Karl (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Peralta, Pedro (Committee member) / Rajagopalan, Jagannathan (Committee member) / Arizona State University (Publisher)
Created2020
Description
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
The intrinsic material properties of diamond are attractive for use in high power limiter/receiver protector (RP) systems, especially the ones required at the input of radio transceivers. The RP device presents a low capacitance and high resistance to low input signals, thereby adding negligible insertion loss to these desired signals. However, at high input radio frequency (RF) power levels, the RP turns on with a resistance much smaller than the 50 Ω characteristic impedance, reflecting most of the potentially damaging input power away from the receiver input. P-type-intrinsic-n-type (PIN) diodes made of Silicon and Gallium Arsenide used in today’s conventional RP systems have certain limitations at high-power. The wide bandgap of diamond combined with its higher thermal conductivity give it a superior RF power handling capability that can protect sensitive RF front-end components from high power incident signals.
Vertical diamond PIN diodes were proposed and fabricated with an n+-i-p++ structure consisting of: a very thin and heavily phosphorus-doped n-type diamond layer and an intrinsic diamond layer grown on a heavily boron-doped diamond substrate with a (111) crystallographic orientation. Direct current (DC) and RF small-signal characterization was carried out by attaching the diamond sample in a shunted coplanar waveguide (CPW) configuration.
The small-signal lumped element model of the diode impedance under forward-bias was validated with a fit to the measured data, and provides a roadmap for the optimization of parameters for the implementation of diamond Schottky PIN diodes to be successfully used in receiver protector/limiter applications at S-band. The experimental results with the device growth and fabrication show promise and can help in further elevating the device RF figure of merit, in turn enabling the path for commercialization of these diamond-based devices.
Vertical diamond PIN diodes were proposed and fabricated with an n+-i-p++ structure consisting of: a very thin and heavily phosphorus-doped n-type diamond layer and an intrinsic diamond layer grown on a heavily boron-doped diamond substrate with a (111) crystallographic orientation. Direct current (DC) and RF small-signal characterization was carried out by attaching the diamond sample in a shunted coplanar waveguide (CPW) configuration.
The small-signal lumped element model of the diode impedance under forward-bias was validated with a fit to the measured data, and provides a roadmap for the optimization of parameters for the implementation of diamond Schottky PIN diodes to be successfully used in receiver protector/limiter applications at S-band. The experimental results with the device growth and fabrication show promise and can help in further elevating the device RF figure of merit, in turn enabling the path for commercialization of these diamond-based devices.
ContributorsAhmad, Mohammad Faizan (Author) / Thornton, Trevor J. (Thesis advisor) / Goodnick, Stephen M. (Committee member) / Nemanich, Robert J. (Committee member) / Arizona State University (Publisher)
Created2020
Description
This dissertation explores the use of deterministic scheduling theory for the design and development of practical manufacturing scheduling strategies as alternatives to current scheduling methods, particularly those used to minimize completion times and increase system capacity utilization. The efficient scheduling of production systems can make the difference between a thriving and a failing enterprise, especially when expanding capacity is limited by the lead time or the high cost of acquiring additional manufacturing resources. A multi-objective optimization (MOO) resource constrained parallel machine scheduling model with setups, machine eligibility restrictions, release and due dates with user interaction is developed for the scheduling of complex manufacturing systems encountered in the semiconductor and plastic injection molding industries, among others. Two mathematical formulations using the time-indexed Integer Programming (IP) model and the Diversity Maximization Approach (DMA) were developed to solve resource constrained problems found in the semiconductor industry. A heuristic was developed to find fast feasible solutions to prime the IP models. The resulting models are applied in two different ways: constructing schedules for tactical decision making and constructing Pareto efficient schedules with user interaction for strategic decision making aiming to provide insight to decision makers on multiple competing objectives.
Optimal solutions were found by the time-indexed IP model for 45 out of 45 scenarios in less than one hour for all the problem instance combinations where setups were not considered. Optimal solutions were found for 18 out of 45 scenarios in less than one hour for several combinations of problem instances with 10 and 25 jobs for the hybrid (IP and heuristic) model considering setups. Regarding the DMA MOO scheduling model, the complete efficient frontier (9 points) was found for a small size problem instance in 8 minutes, and a partial efficient frontier (29 points) was found for a medium sized problem instance in 183 hrs.
Optimal solutions were found by the time-indexed IP model for 45 out of 45 scenarios in less than one hour for all the problem instance combinations where setups were not considered. Optimal solutions were found for 18 out of 45 scenarios in less than one hour for several combinations of problem instances with 10 and 25 jobs for the hybrid (IP and heuristic) model considering setups. Regarding the DMA MOO scheduling model, the complete efficient frontier (9 points) was found for a small size problem instance in 8 minutes, and a partial efficient frontier (29 points) was found for a medium sized problem instance in 183 hrs.
ContributorsMunoz-Estrada, Luis Fernando (Author) / Villalobos, Jesus R (Thesis advisor) / Fowler, John (Thesis advisor) / Rogers, Bradley (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
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