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Description
Resistive random-access memory (RRAM) or memristor, is an emerging technology used in neuromorphic computing to exceed the traditional von Neumann obstacle by merging the processing and memory units. Two-dimensional (2D) materials with non-volatile switching behavior can be used as the switching layer of RRAMs, exhibiting superior behavior compared to conventional oxide-based RRAMs. The use of 2D materials allows scaling the resistive switching layer thickness to sub-nanometer dimensions enabling devices to operate with low switching voltages and high programming speeds, offering large improvements in efficiency and performance as well as ultra-dense integration. This dissertation presents an extensive study of linear and logistic regression algorithms implemented with 1-transistor-1-resistor (1T1R) memristor crossbars arrays. For this task, a simulation platform is used that wraps circuit-level simulations of 1T1R crossbars and physics-based model of RRAM to elucidate the impact of device variability on algorithm accuracy, convergence rate, and precision. Moreover, a smart pulsing strategy is proposed for the practical implementation of synaptic weight updates that can accelerate training in real crossbar architectures.
Next, this dissertation reports on the hardware implementation of analog dot-product operation on arrays of 2D hexagonal boron nitride (h-BN) memristors. This extends beyond previous work that studied isolated device characteristics towards the application of analog neural network accelerators based on 2D memristor arrays. The wafer-level fabrication of the memristor arrays is enabled by large-area transfer of CVD-grown few-layer h-BN films. The dot-product operation shows excellent linearity and repeatability, with low read energy consumption, with minimal error and deviation over various measurement cycles. Moreover, the successful implementation of a stochastic linear and logistic regression algorithm in 2D h-BN memristor hardware is presented for the classification of noisy images. Additionally, the electrical performance of novel 2D h-BN memristor for SNN applications is extensively investigated. Then, using the experimental behavior of the h-BN memristor as the artificial synapse, an unsupervised spiking neural network (SNN) is simulated for the image classification task. A novel and simple Spike-Timing-Dependent-Plasticity (STDP)-based dropout technique is presented to enhance the recognition task of the h-BN memristor-based SNN.
ContributorsAfshari, Sahra (Author) / Sanchez Esqueda, Ivan (Thesis advisor) / Barnaby, Hugh J (Committee member) / Seo, Jae-Sun (Committee member) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2023
Description
The silicon-based solar cell has been extensively deployed in photovoltaic industry and plays an important role in renewable energy industries. A more energy-efficient, environment-harmless and eco-friendly silicon production technique is required for price-competitive solar energy harvesting. Silicon electrorefining in molten salt is promising for the ultrapure solar-grade Si production. To avoid using highly corrosive fluoride salt, CaCl2-based salt is widely employed for silicon electroreduction. For Si electroreduction in CaCl2-based salt, CaO is usually added to enhance the solubility of SiO2. However, the existence of oxygen in molten salt could result in system corrosion, anode passivation and the co-deposition of secondary phases such as CaSiO3 and SiO2 at the cathode. This research focuses on the development of reusable oxygen-free CaCl2-based molten salt for solar-grade silicon electrorefining. A new multi-potential electropurification process has been proposed and proven to be more effective in impurities removal. The as-received salt and the salt after electrorefining have been electropurified. The inductively-coupled plasma mass spectrometry and cyclic voltammetry have been utilized to determine the impurities removal of electropurification. The salt after silicon electrorefining has been regenerated to its original purity level before by the multi-potential electropurification process, demonstrating the feasibility of a reusable salt by electropurification. In an oxygen-free CaCl2-based salt without silicon precursor, the silicon dissolved from the silicon anode can be successfully deposited at the cathode. The silicon anode has been operated for more than 50 hours without passivation in the oxygen-free system. Silicon ions start to be deposited after 0.17 g of silicon has been dissolved into the salt from the silicon anode. A 180 µm deposit with a silver-luster surface was obtained at the cathode. The main impurities in the silicon anode such as aluminum, iron and titanium were not found in the silicon deposits. No oxygen-containing secondary phases are detected in the silicon deposits. These results confirm the feasibility of silicon electrorefining in the oxygen-free CaCl2-based salt.
ContributorsTseng, Mao-Feng (Author) / Tao, Meng (Thesis advisor) / Kannan, Arunachala Mada (Committee member) / Mu, Linqin (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2023
Description
ABSTRACT
With the fast development of industry, it brings indelible pollution to the natural environment. As a consequence, the air quality is getting worse which will seriously affect people's health. With such concern, continuous air quality monitoring and prediction are necessary. Traditional air quality monitoring methods cannot use large amount of historical data to make accurate predic-tions. Moreover, the traditional prediction method can only roughly predict the air quality level in a short time. With the development of artificial intelligence al-gorithms [1] and high performance computing, the latest mathematical methods and algorithms are able to generate much more accurate predictions based on long term past data. In this master thesis project, it explore to develop deep learning based air quality prediction based on real sensor network time series air quality data from STAIR system [3].
ContributorsZhou, Zeming (Author) / Fan, Deliang (Thesis advisor) / Cao, Yu (Committee member) / Yu, Haofei (Committee member) / Arizona State University (Publisher)
Created2023
Description
Quantum computing has the potential to revolutionize the signal-processing field by providing more efficient methods for analyzing signals. This thesis explores the application of quantum computing in signal analysis synthesis for compression applications. More specifically, the study focuses on two key approaches: quantum Fourier transform (QFT) and quantum linear prediction (QLP). The research is motivated by the potential advantages offered by quantum computing in massive signal processing tasks and presents novel quantum circuit designs for QFT, quantum autocorrelation, and QLP, enabling signal analysis synthesis using quantum algorithms. The two approaches are explained as follows. The Quantum Fourier transform (QFT) demonstrates the potential for improved speed in quantum computing compared to classical methods. This thesis focuses on quantum encoding of signals and designing quantum algorithms for signal analysis synthesis, and signal compression using QFTs. Comparative studies are conducted to evaluate quantum computations for Fourier transform applications, considering Signal-to-Noise-Ratio results. The effects of qubit precision and quantum noise are also analyzed. The QFT algorithm is also developed in the J-DSP simulation environment, providing hands-on laboratory experiences for signal-processing students. User-friendly simulation programs on QFT-based signal analysis synthesis using peak picking, and perceptual selection using psychoacoustics in the J-DSP are developed. Further, this research is extended to analyze the autocorrelation of the signal using QFTs and develop a quantum linear prediction (QLP) algorithm for speech processing applications. QFTs and IQFTs are used to compute the quantum autocorrelation of the signal, and the HHL algorithm is modified and used to compute the solutions of the linear equations using quantum computing. The performance of the QLP algorithm is evaluated for system identification, spectral estimation, and speech analysis synthesis, and comparisons are performed for QLP and CLP results. The results demonstrate the following: effective quantum circuits for accurate QFT-based speech analysis synthesis, evaluation of performance with quantum noise, design of accurate quantum autocorrelation, and development of a modified HHL algorithm for efficient QLP. Overall, this thesis contributes to the research on quantum computing for signal processing applications and provides a foundation for further exploration of quantum algorithms for signal analysis synthesis.
ContributorsSharma, Aradhita (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2023
Description
The world has seen a revolution in cellular communication with the advent of 5G (fifth-generation), which enables gigabits per second data speed with low latency, massive capacity, and increased availability. These modern wireless systems improve spectrum efficiency by employing advanced modulation techniques, but result in large peak-to-average power ratios (PAPR) of the transmitted signals that degrades the efficiency of the radio-frequency power amplifiers (PAs) in the power back-off (PBO) region. Envelope tracking (ET), which is a dynamic supply control technology to realize high efficiency PAs, is a promising approach for designing transmitters for the future. Conventional voltage regulators, such as linear regulators and switching regulators, fail to simultaneously offer high speed, high efficiency, and improved linearity. Hybrid supply modulators (HSM) that combine a linear and switching regulator emerge as promising solutions to achieve an optimized tradeoff between different design parameters. Over the years, considerable development and research efforts in industry and academia have been spent on maximizing HSM performance, and a majority of the most recently developed modulators are implemented in CMOS technology and mainly targeted for handset applications. In this dissertation, the main requirements for modern HSM designs are categorized and analyzed in detail. Next, techniques to improve HSM performance are discussed. The available device technologies for HSM and PA implementations are also delineated, and implementation challenges of an integrated ET-PA system are summarized. Finally, a Current-Mode with Dynamic Hysteresis HSM is proposed, designed, and implemented. With the proposed technique, the HSM is able to track LTE signals up to 100 MHz bandwidth. Switching at a peak frequency of 40 MHz, the design is able to track a 1 Vpp sinusoidal signal with high fidelity, has an output voltage ripple around 54 mV, and achieves a peak static and dynamic efficiency of 92.2% and 82.29%, respectively, at the maximum output. The HSM is capable of delivering a maximum output power of 425 mW and occupies a small die area of 1.6mm2. Overall, the proposed HSM promises competitive performance compared to state-of-the-art works.
ContributorsBHARDWAJ, SUMIT (Author) / Kitchen, Jennifer (Thesis advisor) / Ozev, Sule (Committee member) / Bakkaloglu, Bertan (Committee member) / Singh, Shrikant (Committee member) / Arizona State University (Publisher)
Created2024
Description
Power amplifiers and tuneable matching networks for plasma generation systems arebeing continuously advanced, and recent innovations have shown tremendous
improvements in their size, efficiency, and capability. These improvements must
ultimately be validated on a live plasma chamber, but this is costly and time-consuming,
and debugging errors or failures is a challenge owing to the highly dynamic nature of
the plasma and the experimental prototype nature of the advancements. This work
addresses this challenge by developing a reactive load emulation system that can mimic
the inductive reactance of a live plasma chamber. This includes a study of the saturation
characteristics of low-permeability, high-frequency materials, demonstration of the
suitability of this method for plasma emulation, and the design of an inductor array
platform which verifies the approach.
ContributorsTagare, Darshan Ravindra (Author) / Ranjram, Mike (Thesis advisor) / Mallik, Ayan (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2024
Description
Nanoelectronics are electronic components that are often only a few nanometers in size. The field of nanoelectronics encompasses a wide range of products and materials that share the trait of being so small that physical forces can modify their characteristics on a nanoscale. These nanoscale devices are dominated by quantum processes including atomistic disorder and tunneling.In contrast to nanoelectronics, which involves the scaling down of devices to nanoscale levels, molecular electronics is concerned with electronic activities that take place within molecule structures. Detection of molecular conductance plays a vital role in the field of molecular electronics and nanotechnology. The ability to measure the conductive behavior of molecules is necessary to study their surface properties, defects, electronic structures, and for bio-sensing. To determine the conductance of the molecule, it is necessary to deduce the current passing through it. This is achieved by applying a voltage bias across the molecule and the detection instrument. Instruments like Scanning Tunneling Microscope (STM) and chip-based characterization (Probe Station) are used to fetch the amount of current flowing through the molecules. The current through molecules can be very small to measure and needs to be amplified. Linear amplifiers are widely used for amplifying these small currents, but due to their low dynamic range they are being replaced by logarithmic amplifiers. This thesis project aims to customize a logarithmic amplifier design to the interface with these instruments to measure the current flowing through these molecules.
This thesis starts with a review of a linear- current amplifier-based technology that is used for measuring small currents and its challenges. It then introduces logarithmic amplifier for overcoming those obstacles. This thesis involves design, fabrication, and characterization of the built logarithmic amplifier.
Furthermore, the setup includes a custom designed logarithmic amplifier that can be used with instruments like Scanning Tunneling Microscope (STM) and probe station. The key objective of the research is to accurately calibrate the logarithmic amplifier for measurement of currents over a wide range from picoamperes to milliamperes. Dummy resistors with different resistance values are used to replace the sample of which the conductance is to be measured, for testing and calibrating purposes. Bandwidth of the circuit is tested using these different values of resistors.
ContributorsYeole, Aishwarya Yogesh (Author) / Hihath, Josh (Thesis advisor) / Blain Christen, Jennifer (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2024
Description
This thesis explores the development and integration of a wrist-worn pneumatic haptic interface, Pneutouch, into multiplayer virtual reality (VR) environments. The study investigates the impact of haptics on multiplayer experiences, with a specific focus on presence, collaboration, and communication. Evaluation and investigation were performed using three mini-games, each targeting specific interactions and investigating presence, collaboration, and communication. It was found that haptics enhanced user presence and object realism, increased user seriousness towards tasks, and shifted the focus of interactions from user-user to user-object. In collaborative tasks, haptics increased realism but did not improve efficiency for simple tasks. In communication tasks, a unique interaction modality, termed "haptic mirroring," was introduced, which explored a new form of communication that could be implemented with haptic devices. It was found that with new communication modalities, users experience an associated learning curve. Together, these findings suggest a new set of multiplayer haptic design considerations, such as how haptics increase seriousness, shift focus from social to physical interactions, generally increase realism but decrease task efficiency, and have associated learning curves. These findings contribute to the growing body of research on haptics in VR, particularly in multiplayer settings, and provide insights that can be further investigated or utilized in the implementation of VR experiences.
ContributorsManetta, Mason (Author) / LiKamWa, Robert (Thesis advisor) / Lahey, Byron (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2024
Description
Machine learning techniques have found extensive application in dynamic fields like drones, self-driving vehicles, surveillance, and more. Their effectiveness stems from meticulously crafted deep neural networks (DNNs), extensive data gathering efforts, and resource-intensive model training processes. However, due to the unpredictable nature of the environment, these systems will inevitably encounter input samples that deviate from the distribution of their original training data, resulting in instability and performance degradation.To effectively detect the emergence of out-of-distribution (OOD) data, this dissertation first proposes a novel, self-supervised approach that evaluates the Mahalanobis distance between the in-distribution (ID) and OOD in gradient space. A binary classifier is then introduced to guide the label selection for gradients calculation, which further boosts the detection performance.
Next, to continuously adapt the new OOD into the existing knowledge base, an unified framework for novelty detection and continual learning is proposed. The binary classifier, trained to distinguish OOD data from ID, is connected sequentially with the pre-trained model to form a “N + 1” classifier, where “N” represents prior knowledge which contains N classes and “1” refers to the newly arrival OOD. This continual learning process continues as “N+1+1+1+...”, assimilating the knowledge of each new OOD instance into the system.
Finally, this dissertation demonstrates the practical implementation of novelty detection and continual learning within the domain of thermal analysis. To rapidly address the impact of voids in thermal interface material (TIM), a continuous adaptation approach is proposed, which integrates trainable nodes into the graph at the locations where abnormal thermal behaviors are detected. With minimal training overhead, the model can quickly adapts to the change caused by the defects and regenerate accurate thermal prediction.
In summary, this dissertation proposes several algorithms and practical applications in continual learning aimed at enhancing the stability and adaptability of the system. All proposed algorithms are validated through extensive experiments conducted on benchmark datasets such as CIFAR-10, CIFAR-100, TinyImageNet for continual learning, and real thermal data for thermal analysis.
ContributorsSun, Jingbo (Author) / Cao, Yu (Thesis advisor) / Chhabria, Vidya (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Fan, Deliang (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2024
Description
This dissertation discusses continuous-time reinforcement learning (CT-RL) for control of affine nonlinear systems. Continuous-time nonlinear optimal control problems hold great promise in real-world applications. After decades of development, reinforcement learning (RL) has achieved some of the greatest successes as a general nonlinear control design method. Yet as RL control has developed, CT-RL results have greatly lagged their discrete-time RL (DT-RL) counterparts, especially in regards to real-world applications. Current CT-RL algorithms generally fall into two classes: adaptive dynamic programming (ADP), and actor-critic deep RL (DRL). The first school of ADP methods features elegant theoretical results stemming from adaptive and optimal control. Yet, they have not been shown effectively synthesizing meaningful controllers. The second school of DRL has shown impressive learning solutions, yet theoretical guarantees are still to be developed. A substantive analysis uncovering the quantitative causes of the fundamental gap between CT and DT remains to be conducted. Thus, this work develops a first-of-its kind quantitative evaluation framework to diagnose the performance limitations of the leading CT-RL methods. This dissertation also introduces a suite of new CT-RL algorithms which offers both theoretical and synthesis guarantees. The proposed design approach relies on three important factors. First, for physical systems that feature physically-motivated dynamical partitions into distinct loops, the proposed decentralization method breaks the optimal control problem into smaller subproblems. Second, the work introduces a new excitation framework to improve persistence of excitation (PE) and numerical conditioning via classical input/output insights. Third, the method scales the learning problem via design-motivated invertible transformations of the system state variables in order to modulate the algorithm learning regression for further increases in numerical stability. This dissertation introduces a suite of (decentralized) excitable integral reinforcement learning (EIRL) algorithms implementing these paradigms. It rigorously proves convergence, optimality, and closed-loop stability guarantees of the proposed methods, which are demonstrated in comprehensive comparative studies with the leading methods in ADP on a significant application problem of controlling an unstable, nonminimum phase hypersonic vehicle (HSV). It also conducts comprehensive comparative studies with the leading DRL methods on three state-of-the-art (SOTA) environments, revealing new performance/design insights.
ContributorsWallace, Brent Abraham (Author) / Si, Jennie (Thesis advisor) / Berman, Spring M (Committee member) / Bertsekas, Dimitri P (Committee member) / Tsakalis, Konstantinos S (Committee member) / Arizona State University (Publisher)
Created2024