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Description
Wide bandgap semiconductors are of much current interest due to their superior electrical properties. This dissertation describes electron microscopy characterization of GaN-on-GaN structures for high-power vertical device applications. Unintentionally-doped (UID) GaN layers grown homoepitaxially via metal-organic chemical vapor deposition on freestanding GaN substrates, were subjected to dry etching, and layers

Wide bandgap semiconductors are of much current interest due to their superior electrical properties. This dissertation describes electron microscopy characterization of GaN-on-GaN structures for high-power vertical device applications. Unintentionally-doped (UID) GaN layers grown homoepitaxially via metal-organic chemical vapor deposition on freestanding GaN substrates, were subjected to dry etching, and layers of UID-GaN/p-GaN were over-grown. The as-grown and regrown heterostructures were examined in cross-section using transmission electron microscopy (TEM). Two different etching treatments, fast-etch-only and multiple etches with decreasing power, were employed. The fast-etch-only devices showed GaN-on-GaN interface at etched location, and low device breakdown voltages were measured (~ 45-95V). In comparison, no interfaces were visible after multiple etching steps, and the corresponding breakdown voltages were much higher (~1200-1270V). These results emphasized importance of optimizing surface etching techniques for avoiding degraded device performance. The morphology of GaN-on-GaN devices after reverse-bias electrical stressing to breakdown was investigated. All failed devices had irreversible structural damage, showing large surface craters (~15-35 microns deep) with lengthy surface cracks. Cross-sectional TEM of failed devices showed high densities of threading dislocations (TDs) around the cracks and near crater surfaces. Progressive ion-milling across damaged devices revealed high densities of TDs and the presence of voids beneath cracks: these features were not observed in unstressed devices. The morphology of GaN substrates grown by hydride vapor-phase epitaxy (HVPE) and by ammonothermal methods were correlated with reverse-bias results. HVPE substrates showed arrays of surface features when observed by X-ray topography (XRT). All fabricated devices that overlapped with these features had typical reverse-bias voltages less than 100V at a leakage current limit of 10-6 A. In contrast, devices not overlapping with such features reached voltages greater than 300V. After etching, HVPE substrate surfaces showed defect clusters and macro-pits, whereas XRT images of ammonothermal substrate revealed no visible features. However, some devices fabricated on ammonothermal substrate failed at low voltages. Devices on HVPE and ammonothermal substrates with low breakdown voltages showed crater-like surface damage and revealed TDs (~25µm deep) and voids; such features were not observed in devices reaching higher voltages. These results should assist in developing protocols to fabricate reliable high-voltage devices.
ContributorsPeri, Prudhvi Ram (Author) / Smith, David J. (Thesis advisor) / Alford, Terry (Committee member) / Mccartney, Martha R (Committee member) / Nemanich, Robert (Committee member) / Zhao, Yuji (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Over the past decades, the amount of data required to be processed and analyzed by computing systems has been increasing dramatically to exascale (10^18 bytes/s or ops). However, modern computing platforms' inability to deliver both energy-efficient and high-performance computing solutions leads to a gap between meets and needs, especially in

Over the past decades, the amount of data required to be processed and analyzed by computing systems has been increasing dramatically to exascale (10^18 bytes/s or ops). However, modern computing platforms' inability to deliver both energy-efficient and high-performance computing solutions leads to a gap between meets and needs, especially in resource-constraint Internet of Things (IoT) devices. Unfortunately, such a gap will keep widening mainly due to limitations in both devices and architectures. With this motivation, this dissertation's focus is on cross-layer (device/circuit/architecture/application) co-design of energy-efficient and high-performance Processing-in-Memory (PIM) platforms for implementing complex big data applications, i.e., deep learning, bioinformatics, graph processing tasks, and data encryption. The dissertation shows how to leverage innovations from device, circuit, and architecture to integrate memory and logic to break the existing memory and power walls and dramatically increase computing efficiency of today’s non-Von-Neumann computing systems.The proposed PIM platforms transform current volatile and non-volatile random access memory arrays to computational units capable of working as both memory and low-area-overhead, massively parallel, fast, reconfigurable in-memory logic. Instead of integrating complex logic units in cost-sensitive memory, the explored designs exploit hardware-friendly bit-line computing methods to implement complete Boolean logic functions between operands within a memory array in a reduced clock cycle, overcoming the multi-cycle logic issue in modern PIM platforms. Besides, new customized in-memory algorithms and mapping methods are developed to convert the crucial iteratively-used big data application's functions to bit-wise PIM-supported logic. To quantitatively analyze the performance of various PIM platforms running big data applications, a generic and comprehensive evaluation framework is presented. The overall system computing performance (throughput, latency, energy efficiency) for each application is explored through the developed framework. The device-to-algorithm co-simulation results on neural network acceleration demonstrate that the proposed platforms can obtain 36.8× higher energy-efficiency and 22× speed-up compared to state-of-the-art Graphics Processing Unit (GPU). In accelerating bioinformatics tasks such as biological sequence alignment, the presented PIM designs result in ~2×, 43.8×, 458× more throughput per Watt compared to state-of-the-art Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), and GPU platforms, respectively.
ContributorsAngizi, Shaahin (Author) / Fan, Deliang (Thesis advisor) / Seo, Jae-Sun (Committee member) / Awad, Amro (Committee member) / Zhang, Wei (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Quantifying molecular interactions is critical to the understanding of many biological processes and drug screening. To date, various detection techniques have been developed to determine the binding kinetics. However, because most of the mainstream detection technologies detect signals that scale with the mass of ligands bond to the sensor surface,

Quantifying molecular interactions is critical to the understanding of many biological processes and drug screening. To date, various detection techniques have been developed to determine the binding kinetics. However, because most of the mainstream detection technologies detect signals that scale with the mass of ligands bond to the sensor surface, it is still challenging to quantify the binding kinetics of small molecules. To address this problem, two different detection technologies, charge-sensitive optical detection (CSOD) and critical angle reflection (CAR), are developed for label-free detection of molecular interactions with the ability to detect a wide range of molecules including small molecules. In particular, CSOD technique detects the charge rather than the mass of a molecule with an optical fiber. However, the effective charge of a molecule decreases with the buffer ionic strength. For this reason, the previous CSOD works with diluted buffers, which could affect the measured molecular binding kinetics. Here a technique capable of detecting molecular binding kinetics in normal ionic strength buffers is presented. An H-shaped sample well was developed to overcome this problem. With this new design, the binding kinetics between G-protein-coupled receptors (GPCRs) and their small molecule ligands were measured in normal buffer. To further improve the signal-to-noise ratio of CSOD and move it toward high-throughput detection, CSOD was implemented with a quadrant-cell detector to achieve detection in higher frequency range and decrease low-frequency noise.This improved CSOD technique is capable for direct quantification of binding kinetics of phage-displayed peptides to their target protein using the whole phages. CAR imaging can be performed on surface plasmon resonance (SPR) imaging setups. It was shown that CAR is capable of measuring molecular interactions including proteins, nucleic acids and cell-based detections. In addition, it was shown that CAR can detect small molecule bindings and intracellular signals beyond SPR sensing limit. CAR exhibits several distinct characteristics over SPR, including tunable sensitivity and dynamic range, deeper vertical sensing range, and fluorescence compatibility. CAR is anticipated to have the ability to expand SPR capability in small molecule detection, whole cell-based detection, simultaneous fluorescence imaging, and broader conjugation chemistry.
ContributorsLiang, Runli (Author) / Wang, Shaopeng (Thesis advisor) / Blain Christen, Jennifer (Thesis advisor) / Jing, Tianwei (Committee member) / Wang, Chao (Committee member) / Arizona State University (Publisher)
Created2021
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Description
With demand for increased efficiency and smaller carbon footprint, power system operators are striving to improve their modeling, down to the individual consumer device, paving the way for higher production and consumption efficiencies and increased renewable generation without sacrificing system reliability. This dissertation explores two lines of research. The first

With demand for increased efficiency and smaller carbon footprint, power system operators are striving to improve their modeling, down to the individual consumer device, paving the way for higher production and consumption efficiencies and increased renewable generation without sacrificing system reliability. This dissertation explores two lines of research. The first part looks at stochastic continuous-time power system scheduling, where the goal is to better capture system ramping characteristics to address increased variability and uncertainty. The second part of the dissertation starts by developing aggregate population models for residential Demand Response (DR), focusing on storage devices, Electric Vehicles (EVs), Deferrable Appliances (DAs) and Thermostatically Controlled Loads (TCLs). Further, the characteristics of such a population aggregate are explored, such as the resemblance to energy storage devices, and particular attentions is given to how such aggregate models can be considered approximately convex even if the individual resource model is not. Armed with an approximately convex aggregate model for DR, how to interface it with present day energy markets is explored, looking at directions the market could go towards to better accommodate such devices for the benefit of not only the prosumer itself but the system as a whole.
ContributorsHreinsson, Kári (Author) / Scaglione, Anna (Thesis advisor) / Hedman, Kory (Committee member) / Zhang, Junshan (Committee member) / Alizadeh, Mahnoosh (Committee member) / Arizona State University (Publisher)
Created2020
Description
Vertical take-off and landing (VTOL) systems have become a crucial component of aeronautical and commercial applications alike. Quadcopter systems are rather convenient to analyze and design controllers for, owing to symmetry in body dynamics. In this work, a quadcopter model at hover equilibrium is derived, using both high and low

Vertical take-off and landing (VTOL) systems have become a crucial component of aeronautical and commercial applications alike. Quadcopter systems are rather convenient to analyze and design controllers for, owing to symmetry in body dynamics. In this work, a quadcopter model at hover equilibrium is derived, using both high and low level control. The low level control system is designed to track reference Euler angles (roll, pitch and yaw) as shown in previous work [1],[2]. The high level control is designed to track reference X, Y, and Z axis states [3]. The objective of this paper is to model, design and simulate platooning (separation) control for a fleet of 6 quadcopter units, each comprising of high and low level control systems, using a leader-follower approach. The primary motivation of this research is to examine the ”accordion effect”, a phenomenon observed in leader-follower systems due to which positioning or spacing errors arise in follower vehicles due to sudden changes in lead vehicle velocity. It is proposed that the accordion effect occurs when lead vehicle information is not directly communicated with the rest of the system [4][5] . In this paper, the effect of leader acceleration feedback is observed for the quadcopter platoon. This is performed by first designing a classical platoon controller for a nominal case, where communication within the system is purely ad-hoc (i.e from one quadcopter to it’s immediate successor in the fleet). Steady state separation/positioning errors for each member of the fleet are observed and documented during simulation. Following this analysis, lead vehicle acceleration is provided to the controller (as a feed forward term), to observe the extent of it’s effect on steady state separation, specifically along tight maneuvers. Thus the key contribution of this work is a controller that stabilizes a platoon of quadcopters in the presence of the accordion effect, when employing a leader-follower approach. The modeling shown in this paper builds on previous research to design a low costquadcopter platform, the Mark 3 copter [1]. Prior to each simulation, model nonlinearities and hardware constants are measured or derived from the Mark 3 model, in an effort to observe the working of the system in the presence of realistic hardware constraints. The system is designed in compliance with Robot Operating System (ROS) and the Micro Air Vehicle Link (MAVLINK) communication protocol.
ContributorsSrinivasan, Anshuman (Author) / Rodriguez, Armando A. (Thesis advisor) / Si, Jennie (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Over the past few decades, there is an increase in demand for various ground robot applications such as warehouse management, surveillance, mapping, infrastructure inspection, etc. This steady increase in demand has led to a significant rise in the nonholonomic differential drive vehicles (DDV) research. Albeit extensive work has been done

Over the past few decades, there is an increase in demand for various ground robot applications such as warehouse management, surveillance, mapping, infrastructure inspection, etc. This steady increase in demand has led to a significant rise in the nonholonomic differential drive vehicles (DDV) research. Albeit extensive work has been done in developing various control laws for trajectory tracking, point stabilization, formation control, etc., there are still problems and critical questions in regards to design, modeling, and control of DDV’s - that need to be adequately addressed. In this thesis, three different dynamical models are considered that are formed by varying the input/output parameters of the DDV model. These models are analyzed to understand their stability, bandwidth, input-output coupling, and control design properties. Furthermore, a systematic approach has been presented to show the impact of design parameters such as mass, inertia, radius of the wheels, and center of gravity location on the dynamic and inner-loop (speed) control design properties. Subsequently, extensive simulation and hardware trade studies have been conductedto quantify the impact of design parameters and modeling variations on the performance of outer-loop cruise and position control (along a curve). In addition to this, detailed guidelines are provided for when a multi-input multi-output (MIMO) control strategy is advisable over a single-input single-output (SISO) control strategy; when a less stable plant is preferable over a more stable one in order to accommodate performance specifications. Additionally, a multi-robot trajectory tracking implementation based on receding horizon optimization approach is also presented. In most of the optimization-based trajectory tracking approaches found in the literature, only the constraints imposed by the kinematic model are incorporated into the problem. This thesis elaborates the fundamental problem associated with these methods and presents a systematic approach to understand and quantify when kinematic model based constraints are sufficient and when dynamic model-based constraints are necessary to obtain good tracking properties. Detailed instructions are given for designing and building the DDV based on performance specifications, and also, an open-source platform capable of handling high-speed multi-robot research is developed in C++.
ContributorsManne, Sai Sravan (Author) / Rodriguez, Armando A (Thesis advisor) / Si, Jennie (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Modular multilevel converter (MMC) has become the most attractive and promising topology for multi-terminal high-voltage direct current (MTDC) transmission system. Currently, the dq controller and droop controller are widely used in MTDC systems. However, dq control needs phase synchronization by the phase-locked loop (PLL) and ignores the MMC inner dynamics,

Modular multilevel converter (MMC) has become the most attractive and promising topology for multi-terminal high-voltage direct current (MTDC) transmission system. Currently, the dq controller and droop controller are widely used in MTDC systems. However, dq control needs phase synchronization by the phase-locked loop (PLL) and ignores the MMC inner dynamics, which jeopardizes the power decoupling and system stability. On the other side, inappropriate droop parameters can cause instability due to the complicated dynamics of MTDC systems. Moreover, the estimation of control parameters stability region will be helpful to guarantee safe operation of the MMC-MTDC systems. In this thesis, a generalized model of the MMC-MTDC systems is developed, which is precise to reflect transient dynamics, and applicable for arbitrary dc network topology and transmission line model. Furthermore, a nonlinear phase-unsynchronized power decoupling control for MMC is proposed. It realizes power decoupling without PLL and MMC output power dynamics are designed as second-order inertial systems for convenient parameter determination. Additionally, a nonlinear droop controller with a reference self-correct algorithm is proposed for improving regulation speed, reducing dc voltage deviation, and maintaining stability. For convenient stability analysis, an inequality-constraint-based method is proposed to efficiently estimate parameter stability regions through constructing the inequality constraints of parameters' variation. To verify the proposed methods, 4-terminal and 14-terminal MMC-MTDC systems are employed. A comparison of dynamic responses between the calculations of nonlinear state-space models in MATLAB and the EMT simulations in PSCAD/EMTDC is conducted to demonstrate the accuracy of the developed model. Then, the proposed phase-unsynchronized power decoupling control is verified by four cases in EMT simulations and four cases in the experimental prototype. Meanwhile, comparisons with the dq control are conducted to demonstrate the benefits of the proposed method. Furthermore, the zero dynamic stability is investigated and the influences of system parameters on stability are analyzed. For the MTDC control, the performance of the proposed nonlinear droop control is validated in the EMT simulations. At last, the effectiveness of the proposed estimation method of parameter stability regions is demonstrated by several examinations including the supremum tests of droop slopes, the stability region sketches on the accuracy, and the unstable operations with predicted improper droop slopes.
ContributorsZou, Yuntao (Author) / Qin, Jiangchao JQ (Thesis advisor) / Vittal, Vijay VV (Committee member) / Ayyanar, Raja RA (Committee member) / Wu, Meng MW (Committee member) / Arizona State University (Publisher)
Created2021
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Description
This paper introduces an application space of Power over Ethernet to Universal Serial Bus (USB) Power Delivery, and develops 3 different flyback approaches to a 45 Watt solution in the space. The designs of Fixed Frequency Flyback, Quasi-Resonant Flyback, and Active Clamp Flyback are developed for the application with 37

This paper introduces an application space of Power over Ethernet to Universal Serial Bus (USB) Power Delivery, and develops 3 different flyback approaches to a 45 Watt solution in the space. The designs of Fixed Frequency Flyback, Quasi-Resonant Flyback, and Active Clamp Flyback are developed for the application with 37 Volts (V) to 57 V Direct Current (DC) input voltage and 5 V, 9 V, 15 V, and 20 V output, and results are examined for the given specifications. Implementation based concerns are addressed for each topology during the design process. The systems are proven and tested for efficiency, thermals, and output voltage ripple across the operation range. The topologies are then compared for a cost and benefit analysis and their highlights are identified to showcase each systems prowess.
ContributorsNasir, Anthony Michael (Author) / Ayyanar, Raja (Thesis advisor) / Lei, Qin (Committee member) / Hari, Ajay (Committee member) / Arizona State University (Publisher)
Created2021
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Description
A distributed wireless sensor network (WSN) is a network of a large number of lowcost,multi-functional sensors with power, bandwidth, and memory constraints, operating in remote environments with sensing and communication capabilities. WSNs are a source for a large amount of data and due to the inherent communication and resource constraints, developing a distributed

A distributed wireless sensor network (WSN) is a network of a large number of lowcost,multi-functional sensors with power, bandwidth, and memory constraints, operating in remote environments with sensing and communication capabilities. WSNs are a source for a large amount of data and due to the inherent communication and resource constraints, developing a distributed algorithms to perform statistical parameter estimation and data analysis is necessary. In this work, consensus based distributed algorithms are developed for distributed estimation and processing over WSNs. Firstly, a distributed spectral clustering algorithm to group the sensors based on the location attributes is developed. Next, a distributed max consensus algorithm robust to additive noise in the network is designed. Furthermore, distributed spectral radius estimation algorithms for analog, as well as, digital communication models are developed. The proposed algorithms work for any connected graph topologies. Theoretical bounds are derived and simulation results supporting the theory are also presented.
ContributorsMuniraju, Gowtham (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Berisha, Visar (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2021
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Description
As the field of machine learning increasingly provides real value to power system operations, the availability of rich measurement datasets has become crucial for the development of new applications and technologies. This dissertation focuses on the use of time-series load data for the design of novel data-driven algorithms. Loads are

As the field of machine learning increasingly provides real value to power system operations, the availability of rich measurement datasets has become crucial for the development of new applications and technologies. This dissertation focuses on the use of time-series load data for the design of novel data-driven algorithms. Loads are one of the main factors driving the behavior of a power system and they depend on external phenomena which are not captured by traditional simulation tools. Thus, accurate models that capture the fundamental characteristics of time-series load dataare necessary. In the first part of this dissertation, an example of successful application of machine learning algorithms that leverage load data is presented. Prior work has shown that power systems energy management systems are vulnerable to false data injection attacks against state estimation. Here, a data-driven approach for the detection and localization of such attacks is proposed. The detector uses historical data to learn the normal behavior of the loads in a system and subsequently identify if any of the real-time observed measurements are being manipulated by an attacker. The second part of this work focuses on the design of generative models for time-series load data. Two separate techniques are used to learn load behaviors from real datasets and exploiting them to generate realistic synthetic data. The first approach is based on principal component analysis (PCA), which is used to extract common temporal patterns from real data. The second method leverages conditional generative adversarial networks (cGANs) and it overcomes the limitations of the PCA-based model while providing greater and more nuanced control on the generation of specific types of load profiles. Finally, these two classes of models are combined in a multi-resolution generative scheme which is capable of producing any amount of time-series load data at any sampling resolution, for lengths ranging from a few seconds to years.
ContributorsPinceti, Andrea (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Committee member) / Pal, Anamitra (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2021