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Description
Wide bandgap semiconductors, also known as WBG semiconductors are materials which have larger bandgaps than conventional semiconductors such as Si or GaAs. They permit devices to operate at much higher voltages, frequencies and temperatures. They are the key material used to make LEDs, lasers, radio frequency applications, military applications, and power electronics. Their intrinsic qualities make them promising for next-generation devices for general semiconductor use. Their ability to handle higher power density is particularly attractive for attempts to sustain Moore's law, as conventional technologies appear to be reaching a bottleneck. Apart from WBG materials, ultra-wide bandgap (UWBG) materials, such as Ga2O3, AlN, diamond, or BN, are also attractive since they have even more extreme properties. Although this field is relatively new, which still remains a lot of effort to study and investigate, people can still expect that these materials could be the main characters for more advanced applications in the near future.
In the dissertation, three topics with power devices made by WBG or UWBG semiconductors were introduced. In chapter 1, a generally background knowledge introduction is given. This helps the reader to learn current research focuses. In chapter 2, a comprehensive study of temperature-dependent characteristics of Ga2O3 SBDs with highly-doped substrate is demonstrated. A modified thermionic emission model over an inhomogeneous barrier with a voltage-dependent barrier height is investigated. Besides, the mechanism of surface leakage current is also discussed. These results are beneficial for future developments of low-loss β-Ga2O3 electronics and optoelectronics. In chapter 3, vertical GaN Schottky barrier diodes (SBDs) with floating metal rings (FMRs) as edge termination structures on bulk GaN substrates was introduced. This work represents a useful reference for the FMR termination design for GaN power devices. In chapter 4, AlGaN/GaN metal-insulator-semiconductor high electron mobility transistors (MISHEMTs) fabricated on Si substrates with a 10 nm boron nitride (BN) layer as gate dielectric was demonstrated. The material characterization was investigated by X-ray photoelectric spectroscopy (XPS) and UV photoelectron spectroscopy (UPS). And the gate leakage current mechanisms were also investigated by temperature-dependent current-voltage measurements.
Although still in its infancy, past and projected future progress of electronic designs will ultimately achieve this very goal that WBG and UWBG semiconductors will be indispensable for today and future’s science, technologies and society.
ContributorsYang, Tsung-Han (Author) / Zhao, Yuji (Thesis advisor) / Vasileska, Dragica (Committee member) / Yu, Hongbin (Committee member) / Nemanich, Robert (Committee member) / Arizona State University (Publisher)
Created2021
Description
This research presents advances in time-synchronized phasor (i.e.,synchrophasor) estimation and imaging with very-low-frequency electric fields.
Phasor measurement units measure and track dynamic systems, often power
systems, using synchrophasor estimation algorithms. Two improvements to
subspace-based synchrophasor estimation algorithms are shown. The first
improvement is a dynamic thresholding method for accurately determining the
signal subspace when using the estimation of signal parameters via rotational
invariance techniques (ESPRIT) algorithm. This improvement facilitates
accurate ESPRIT-based frequency estimates of both the nominal system frequency
and the frequencies of interfering signals such as harmonics or out-of-band
interference signals. Proper frequency estimation of all signals present in
measurement data allows for accurate least squares estimates of synchrophasors
for the nominal system frequency. By including the effects of clutter signals
in the synchrophasor estimate, interference from clutter signals can be
excluded. The result is near-flat estimation error during nominal system
frequency changes, the presence of harmonic distortion, and out-of-band
interference. The second improvement reduces the computational burden of the
ESPRIT frequency estimation step by showing that an optimized Eigenvalue
decomposition of the measurement data can be used instead of a singular value
decomposition. This research also explores a deep-learning-based inversion
method for imaging objects with a uniform electric field and a 2D planar D-dot
array. Using electric fields as an illumination source has seen multiple
applications ranging from medical imaging to mineral deposit detection. It is
shown that a planar D-dot array and deep neural network can reconstruct the
electrical properties of randomized objects. A 16000-sample dataset of objects
comprised of a three-by-three grid of randomized dielectric constants was
generated to train a deep neural network for predicting these dielectric
constants from measured field distortions. Increasingly complex imaging
environments are simulated, ranging from objects in free space to objects
placed in a physical cage designed to produce uniform electric fields.
Finally, this research relaxes the uniform electric field constraint, showing
that the volume of an opaque container can be imaged with a copper tube antenna
and a 1x4 array of D-dot sensors. Real world experimental results
show that it is possible to image buckets of water (targets) within a plastic
shed These experiments explore the detectability of targets as a function of
target placement within the shed.
ContributorsDrummond, Zachary (Author) / Allee, David R (Thesis advisor) / Claytor, Kevin E (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Aberle, James (Committee member) / Arizona State University (Publisher)
Created2021
Description
Wurtzite (B, Ga, Al) N semiconductors, especially (Ga, Al) N material systems, demonstrate immense promises to boost the economic growth in the semiconductor industry that is approaching the end of Moore’s law. At the material level, their high electric field strength, high saturation velocity, and unique heterojunction polarization charge have enabled tremendous potentials for high power, high frequency, and photonic applications. With the availability of large-area bulk GaN substrates and high-quality epilayer on foreign substrates, the power conversion applications of GaN are now at the cusp of commercialization.Despite these encouraging advances, there remain two critical hurdles in GaN-based technology: selective area doping and hole-based p-channel devices. Current selective area doping methods are still immature and lead to low-quality lateral p-n junctions, which prevent the realization of advanced power transistors and rectifiers. The missing of hole-based p-channel devices hinders the development of GaN complementary integrated circuits.
This thesis comprehensively studied these challenges. The first part (chapter 2) researched the selective area doping by etch-then-regrow. A GaN-based vertical-channel junction field-effect transistors (VC-JFETs) was experimentally demonstrated by blanket regrowth and self-planarization. The devices’ electrical performances were characterized to understand the regrowth quality. The non-ideal factors during p-GaN regrowth were also discussed. The second part (chapter 3-5) systematically studied the application of the hydrogen plasma treatment process to change the p-GaN properties selectively. A novel GaN-based metal-insulator-semiconductor junction was demonstrated. Then a novel edge termination design with avalanche breakdown capability achieved in GaN power rectifiers is proposed. The last part (Chapter 6) demonstrated a GaN-based p-channel heterojunction field-effect transistor, with record low leakage, subthreshold swing, and a record high on/off ratio. In the end, some outlook and future work have also been proposed.
Although in infancy, the demonstrated etch-then-regrow and the hydrogen plasma treatment methods have the potential to ultimately solve the challenges in GaN and benefit the development of the wide-ultra-wide bandgap industry, technology, and society.
ContributorsYang, Chen (Author) / Zhao, Yuji (Thesis advisor) / Goodnick, Stephen (Committee member) / Yu, Hongbin (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2021
Description
Modern radio frequency (RF) sensors are digital systems characterized by wide band frequency range, and capable to perform multi-function tasks such as: radar, electronic warfare (EW), and communications simultaneously on different sub-arrays. This demands careful understanding of the behavior of each sub-system and how each sub-array interacts with the others. A way to estimate and measure the active reflection coefficient (ARC) to calculate the active voltage standing wave ratio (VSWR) of multiple input multiple output (MIMO) radar when elements (or sub-arrays) are driven with different waveforms has been developed. This technique will help to understand and incorporate bounds in the design of MIMO systems and its waveforms to avoid damages by large power reflections and to improve system performance. The methodology developed consists of evaluating the active VSWR at each individual antenna element or sub-array from (1) estimates of the ARC by using computational electromagnetic (CEM) tools or (2) by directly measuring the ARC at each antenna element or sub-array. The former methodology is important especially at the design phase where trade offs between element shapes and geometrical configurations are taking place. The former methodology is expanded by directly measuring ARC using an experimental radar testbed Baseband-digital at Every Element MIMO Experimental Radar (BEEMER) system to assess the active VSWR, side-lobe levels and antenna pattern effects when different waveforms are transmitted. An optimization technique is implemented to mitigate the effects of the ARC in co-located MIMO radars by waveform design.
ContributorsColonDiaz, Nivia (Author) / Aberle, James T. (Thesis advisor) / Bliss, Daniel W. (Thesis advisor) / Diaz, Rodolfo (Committee member) / Janning, Dan (Committee member) / Arizona State University (Publisher)
Created2021
Forward and Backward Machine Learning for Modeling Copper Diffusion in Cadmium Telluride Solar Cells
Description
To optimize solar cell performance, it is necessary to properly design the doping profile in the absorber layer of the solar cell. For CdTe solar cells, Cu is used for providing p-type doping. Hence, having an estimator that, given the diffusion parameter set (time and Temperature) and the doping concentration at the junction, gives the junction depth of the absorber layer, is essential in the design process of CdTe solar cells (and other cell technologies). In this work it is called a forward (direct) estimation process. The backward (inverse) problem then is the one in which, given the junction depth and the desired concentration of Cu doping at the CdTe/CdS heterointerface, the estimator gives the time and/or the Temperature needed to achieve the desired doping profiles. This is called a backward (inverse) estimation process. Such estimators, both forward and backward, do not exist in the literature for solar cell technology. To train the Machine Learning (ML) estimator, it is necessary to first generate a large set of data that are obtained by using the PVRD-FASP Solver, which has been validated via comparison with experimental values. Note that this big dataset needs to be generated only once. Next, one uses Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) to extract the actual Cu doping profiles that result from the process of diffusion, annealing, and cool-down in the fabrication sequence of CdTe solar cells. Two deep learning neural network models are used: (1) Multilayer Perceptron Artificial Neural Network (MLPANN) model using a Keras Application Programmable Interface (API) with TensorFlow backend, and (2) Radial Basis Function Network (RBFN) model to predict the Cu doping profiles for different Temperatures and durations of the annealing process. Excellent agreement between the simulated results obtained with the PVRD-FASP Solver and the predicted values is obtained. It is important to mention here that it takes a significant amount of time to generate the Cu doping profiles given the initial conditions using the PVRD-FASP Solver, because solving the drift-diffusion-reaction model is mathematically a stiff problem and leads to numerical instabilities if the time steps are not small enough, which, in turn, affects the time needed for completion of one simulation run. The generation of the same with Machine Learning (ML) is almost instantaneous and can serve as an excellent simulation tool to guide future fabrication of optimal doping profiles in CdTe solar cells.
ContributorsSalman, Ghaith (Author) / Vasileska, Dragica (Thesis advisor) / Goodnick, Stephen M. (Thesis advisor) / Ringhofer, Christian (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2021
Description
The power-flow problem has been solved using the Newton-Raphson and Gauss-Seidel methods. Recently the holomorphic embedding method (HEM), a recursive (non-iterative) method applied to solving nonlinear algebraic systems, was applied to the power-flow problem. HEM has been claimed to have superior properties when compared to the Newton-Raphson and other iterative methods in the sense that if the power-flow solution exists, it is guaranteed that a properly configured HEM can find the high voltage solution and, if no solution exists, HEM will signal that unequivocally. Provided a solution exists, convergence of HEM in the extremal domain is claimed to be theoretically guaranteed by Stahl’s convergence-in-capacity theorem, another advantage over other iterative nonlinear solver.In this work it is shown that the poles and zeros of the rational function from fitting the local PMU measurements can be used theoretically to predict the voltage-collapse point. Different numerical methods were applied to improve prediction accuracy when measurement noise is present.
It is also shown in this work that the dc optimal power flow (DCOPF) problem can be formulated as a properly embedded set of algebraic equations. Consequently, HEM may also be used to advantage on the DCOPF problem. For the systems examined, the HEM-based interior-point approach can be used to solve the DCOPF problem.
While the ultimate goal of this line of research is to solve the ac OPF; tackled in this work, is a precursor and well-known problem with Padé approximants: spurious poles that are generated when calculating the Padé approximant may, at times, prevent convergence within the functions domain. A new method for calculating the Padé approximant, called the Padé Matrix Pencil Method was developed to solve the spurious pole problem. The Padé Matrix Pencil Method can achieve accuracy equal to that of the so-called direct method for calculating Padé approximants of the voltage-functions tested while both using a reduced order approximant and eliminating any spurious poles within the portion of the function’s domain of interest: the real axis of the complex plane up to the saddle-node bifurcation point.
ContributorsLi, Songyan (Author) / Tylavsky, Daniel (Thesis advisor) / Ayyanar, Raja (Committee member) / Weng, Yang (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2021
Description
Due to the large scale of power systems, latency uncertainty in communication can cause severe problems in wide-area measurement systems. To resolve the issue, a significant amount of past work focuses on using emerging technologywhich is machine learning methods such as Q-learning to address latency issues in modern controls. Although such a method can deal with the stochastic characteristics of communication latency in the long run, the Q-learning methods tend to overestimate Q-values, leading to high bias. To solve the overestimation bias issue, the learning structure is redesigned with a twin-delayed deep deterministic policy gradient algorithm to handle the damping control issue under unknown latency in the power network. Meanwhile, a new reward function is proposed, taking into account the machine speed deviation, the episode termination prevention, and the feedback from action space. In this way, the system optimally damps down frequency oscillations while maintaining the system’s stability and reliable operation within defined limits. The simulation results verify the proposed algorithm in various perspectives including the latency sensitivity analysis under high renewable energy penetration and the comparison with other machine learning algorithms.
For example, if the proposed twin-delayed deep deterministic policy gradient algorithm is applied, the low-frequency oscillation significantly improved compared to existing algorithms.
Furthermore, under the mentorship of Dr. Yang Weng, the development of a big data analysis software project has been collaborating with the Salt River Project (SRP), a major power utility in Arizona. After a thorough examination of data for
the project, it is examined that SRP is suffering from a lot of smart meters data issues. An important goal of the project is to design big data software to monitor SRP smart meter data and to present indicators of abnormalities and special
events. Currently, the big data software interface has been developed for SRP, which has already been successfully adopted by other utilities, research institutes, and laboratories as well.
ContributorsKim, Gyoungjae (Author) / Weng, Yang (Thesis advisor) / Wu, Meng (Committee member) / Zhao, Yunpeng (Committee member) / Arizona State University (Publisher)
Created2021
Description
Artificial intelligence is one of the leading technologies that mimics the problem solving and decision making capabilities of the human brain. Machine learning algorithms, especially deep learning algorithms, are leading the way in terms of performance and robustness. They are used for various purposes, mainly for computer vision, speech recognition, and object detection. The algorithms are usually tested inaccuracy, and they utilize full floating-point precision (32 bits). The hardware would require a high amount of power and area to accommodate many parameters with full precision. In this exploratory work, the convolution autoencoder is quantized for the working of an event base camera. The model is designed so that the autoencoder can work on-chip, which would sufficiently decrease the latency in processing. Different quantization methods are used to quantize and binarize the weights and activations of this neural network model to be portable and power efficient. The sparsity term is added to make the model as robust and energy-efficient as possible. The network model was able to recoup the lost accuracy due to binarizing the weights and activation's to quantize the layers of the encoder selectively. This method of recouping the accuracy gives enough flexibility to introduce the network on the chip to get real-time processing from systems like event-based cameras. Lately, computer vision, especially object detection have made strides in their object detection accuracy. The algorithms can sufficiently detect and predict the objects in real-time. However, end-to-end detection of the algorithm is challenging due to the large parameter need and processing requirements. A change in the Non Maximum Suppression algorithm in SSD(Single Shot Detector)-Mobilenet-V1 resulted in less computational complexity without change in the quality of output metric. The Mean Average Precision(mAP) calculated suggests that this method can be implemented in the post-processing of other networks.
ContributorsKuzhively, Ajay Balu (Author) / Cao, Yu (Thesis advisor) / Seo, Jae-Sun (Committee member) / Fan, Delian (Committee member) / Arizona State University (Publisher)
Created2021
Description
Nowadays, the widespread use of distributed generators (DGs) raises significant challenges for the design, planning, and operation of power systems. To avoid the harm caused by excessive DGs, evaluating the reliability and sustainability of the system with high penetration of DGs is essential. The concept of hosting capacity (HC) is used to achieve this purpose. It is to assess the capability of a distribution grid to accommodate DGs without causing damage or updating facilities. To obtain the HC value, traditional HC analysis methods face many problems, including the computational difficulties caused by the large-scale simulations and calculations, lacking the considering temporal correlation from data to data, and the inefficient on real-time analysis. This paper proposes a machine learning-based method, the Spatial-Temporal Long Short-Term Memory (ST-LSTM), to overcome these drawbacks using the traditional HC analysis method. This method will significantly reduce the requirement of calculations and simulations, and obtain HC results in real-time. Using the time-series load profiles and the longest path method, ST-LSTMs can capture the temporal information and spatial information respectively. Moreover, compared with the basic Long Short-Term Memory (LSTM) model, this modified model will improve the performance in the HC analysis by some specific designs, which are the sensitivity gate to consider voltage sensitivity information, the dual forget gates to build spatial and temporal correlation.
ContributorsWu, Jiaqi (Author) / Weng, Yang (Thesis advisor) / Ayyanar, Raja (Committee member) / Cook, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2021
Description
This thesis presents three novel studies. The first two works focus on galvanically isolated chip-to-chip communication, and the third research studies class-E pulse-width modulated power amplifiers. First, a common-mode resilient CMOS (complementary metal-oxide-semiconductor) galvanically isolated Radio Frequency (RF) chip-to-chip communication system is presented utilizing laterally resonant coupled circuits to increases maximum common-mode transient immunity and the isolation capability of galvanic isolators in a low-cost standard CMOS solution beyond the limits provided from the vertical coupling. The design provides the highest reported CMTI (common-mode transient immunity) of more than 600 kV/µs, 5 kVpk isolation, and a chip area of 0.95 mm2. In the second work, a bi-directional ultra-wideband transformer-coupled galvanic isolator is reported for the first time. The proposed design merges the functionality of two isolated channels into one magnetically coupled communication, enabling up to 50% form-factor and assembly cost reduction while achieving a simultaneously robust and state-of-art performance. This work achieves simultaneous robust, wideband, and energy-efficient performance of 300 Mb/s data rate, isolation of 7.8 kVrms, and power consumption and propagation delay of 200 pJ/b and 5 ns, respectively, in only 0.8 mm2 area. The third works studies class-E pulse-width modulated (PWM) Power amplifiers (PAs). For the first time, it presents a design technique to significantly extend the Power back-off (PBO) dynamic range of PWM PAs over the prior art. A proof-of-concept watt-level class-E PA is designed using a GaN HEMT and exhibits more than 6dB dynamic range for a 50 to 30 percent duty cycle variation. Moreover, in this work, the effects of non-idealities on performance and design of class-E power amplifiers for variable supply on and pulse-width operations are characterized and studied, including the effect of non-linear parasitic capacitances and its exploitation for enhancement of average efficiency and self-heating effects in class-E SMPAs using a new over dry-ice measurement technique was presented for this first time. The non-ideality study allows for capturing a full view of the design requirement and considerations of class-E power amplifiers and provides a window to the phenomena that lead to a mismatch between the ideal and actual performance of class-E power amplifiers and their root causes.
ContributorsJavidahmadabadi, Mahdi (Author) / Kitchen, Jennifer N (Thesis advisor) / Aberle, James (Committee member) / Bakkaloglu, Bertan (Committee member) / Burton, Richard (Committee member) / Arizona State University (Publisher)
Created2021