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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
Description
Atmospheric turbulence distorts the path of light passing through the air. When capturing images at long range, the effects of this turbulence can cause substantial geometric distortion and blur in images and videos, degrading image quality. These become more pronounced with greater turbulence, scaling with the refractive index structure constant, Cn2. Removing effects of atmospheric turbulence in images has a range of applications from astronomical imaging to surveillance. Thus, there is great utility in transforming a turbulent image into a ``clean image" undegraded by turbulence. However, as the turbulence is space- and time-variant and statistically random, no closed-form solution exists for a function that performs this transformation. Prior attempts to approximate the solution include spatio-temporal models and lucky frames models, which require many images to provide a good approximation, and supervised neural networks, which rely on large amounts of simulated or difficult-to-acquire real training data and can struggle to generalize. The first contribution in this thesis is an unsupervised neural-network-based model to perform image restoration for atmospheric turbulence with state-of-the-art performance. The model consists of a grid deformer, which produces an estimated distortion field, and an image generator, which estimates the distortion-free image. This model is transferable across different datasets; its efficacy is demonstrated across multiple datasets and on both air and water turbulence. The second contribution is a supervised neural network to predict Cn2 directly from the warp field. This network was trained on a wide range of Cn2 values and estimates Cn2 with relatively good accuracy. When used on the warp field produced by the unsupervised model, this allows for a Cn2 estimate requiring only a few images without any prior knowledge of ground truth or information about the turbulence.
ContributorsWhyte, Cameron (Author) / Jayasuriya, Suren (Thesis advisor) / Espanol, Malena (Thesis advisor) / Speyer, Gil (Committee member) / Arizona State University (Publisher)
Created2021
Description
This work focuses on the analysis and design of large-scale millimeter-wave andterahertz (mmWave/THz) beamforming apertures (e.g., reconfigurable reflective surfaces–
RRSs). As such, the small wavelengths and ample bandwidths of these frequencies enable
the development of high-spatial-resolution imaging and high-throughput wireless
communication systems that leverage electrically large apertures to form high-gain
steerable beams.
For the rigorous evaluation of these systems’ performance in realistic application
scenarios, full-wave simulations are needed to capture all the exhibited electromagnetic
phenomena. However, the small wavelengths of mmWave/THz bands lead to enormous
meshes in conventional full-wave simulators. Thus, a novel numerical decomposition
technique is presented, which decomposes the full-wave models in smaller domains with
less meshed elements, enabling their computationally efficient analysis. Thereafter, this
method is leveraged to study a novel radar configuration that employs a rotating linear
antenna with beam steering capabilities to form 3D images. This imaging process requires
fewer elements to carry out high-spatial-resolution imaging compared to traditional 2D
phased arrays, constituting a perfect candidate in low-profile, low-cost applications.
Afterward, a high-yield nanofabrication technique for mmWave/THz graphene
switches is presented. The measured graphene sheet impedances are incorporated into
equivalent circuit models of coplanar switches to identify the optimum mmWave/THz
switch topology that would enable the development of large-scale RRSs.ii
Thereon, the process of integrating the optimized graphene switches into largescale mmWave/THz RRSs is detailed. The resulting RRSs enable dynamic beam steering
achieving 4-bits of phase quantization –for the first time in the known literature–
eliminating the parasitic lobes and increasing the aperture efficiency. Furthermore, the
devised multi-bit configurations use a single switch-per-bit topology retaining low system
complexity and RF losses. Finally, single-bit RRSs are modified to offer single-lobe
patterns by employing a surface randomization technique. This approach allows for the use
of low-complexity single-bit configurations to suppress the undesired quantization lobes
without residing to the use of sophisticated multi-bit topologies.
The presented concepts pave the road toward the implementation and proliferation
of large-scale reconfigurable beamforming apertures that can serve both as mmWave/THz
imagers and as relays or base stations in future wireless communication applications.
ContributorsTheofanopoulos, Panagiotis (Author) / Trichopoulos, Georgios (Thesis advisor) / Balanis, Constantine (Committee member) / Aberle, James (Committee member) / Bliss, Dan (Committee member) / Groppi, Christopher (Committee member) / Arizona State University (Publisher)
Created2021
Description
Point-of-Care diagnostics is one of the most popular fields of research in bio-medicine today because of its portability, speed of response, convenience and quality assurance. One of the most important steps in such a device is to prepare and purify the sample by extracting the nucleic acids, for which small spherical magnetic particles called magnetic beads are often used in laboratories. Even though magnetic beads have the ability to isolate DNA or RNA from bio-samples in their purified form, integrating these into a microfluidic point-of-need testing kit is still a bit of a challenge. In this thesis, the possibility of integrating paramagnetic beads instead of silica-coated dynabeads, has been evaluated with respect to a point-of-need SARS-CoV-2 virus testing kit. This project is a comparative study between five different sizes of carboxyl-coated paramagnetic beads with reference to silica-coated dynabeads, and how each of them behave in a microcapillary chip in presence of magnetic fields of different strengths. The diameters and velocities of the beads have been calculated using different types of microscopic imaging techniques. The washing and elution steps of an extraction process have been recreated using syringe pump, microcapillary channels and permanent magnets, based on which those parameters of the beads have been studied which are essential for extraction behaviour. The yield efficiency of the beads have also been analysed by using these to extract Salmon DNA. Overall, furthering this research will improve the sensitivity and specificity for any low-cost nucleic-acid based point-of-care testing device.
ContributorsBiswas, Shilpita (Author) / Christen, Jennifer B (Thesis advisor) / Ozev, Sule (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2021
Description
Due to high DRAM access latency and energy, several convolutional neural network(CNN) accelerators face performance and energy efficiency challenges, which are critical for embedded implementations. As these applications exploit larger datasets, memory accesses of these emerging applications are increasing. As a result, it is difficult to predict the combined dynamic random access memory (DRAM) workload behavior, which can sabotage memory optimizations in software. To understand the impact of external memory access on CNN accelerators which reduces the high DRAMaccess latency and energy, simulators such as RAMULATOR and VAMPIRE have been proposed in prior work. In this work, we utilize these simulators to benchmark external memory access in CNN accelerators. Experiments are performed generating trace files based on the number of parameters and data precision and also using trace file generated for CNN Accelerator Altera Arria 10 GX 1150 FPGA data to complete the end to end workflow using the mentioned simulators. Besides that, certain modifications were made in the default VAMPIRE code to implement certain functionalities such as PREA(Precharge All) and REF(Refresh). Then, precalculated energies were computed for DDR3, DDR4, and HBM based on the micron model to mention it in the dram specification file inputted to the VAMPIRE tool. An experimental study was performed and a comparison is made between DDR3, DDR4, and HBM, it was proved that DDR4 is nearly 31% more energy-efficient than DDR3 and HBMis 54% energy-efficient than DDR3. Performed modeling and experimental analysis on a large set of data and then split it into a set of data and compared the results of the small sets multiplied with the number of sets and the large data set and concluded that the results were nearly the same. Finally, a GUI is developed by wrapping both the simulators. GUI provides user-friendly access and can analyze the parameters without much prior knowledge and understanding of the working.
ContributorsPannala, Manvitha (Author) / Cao, Yu (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2021
Description
With the formation of next generation wireless communication, a growing number of new applications like internet of things, autonomous car, and drone is crowding the unlicensed spectrum. Licensed network such as LTE also comes to the unlicensed spectrum for better providing high-capacity contents with low cost. However, LTE was not designed for sharing spectrum with others. A cooperation center for these networks is costly because they possess heterogeneous properties and everyone can enter and leave the spectrum unrestrictedly, so the design will be challenging. Since it is infeasible to incorporate potentially infinite scenarios with one unified design, an alternative solution is to let each network learn its own coexistence policy. Previous solutions only work on fixed scenarios. In this work we present a reinforcement learning algorithm to cope with the coexistence between Wi-Fi and LTE-LAA agents in 5 GHz unlicensed spectrum. The coexistence problem was modeled as a Dec-POMDP and Bayesian approach was adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents. A fairness measure was introduced in the reward function to encourage fair sharing between agents. We turned the reinforcement learning into an optimization problem by transforming the value function as likelihood and variational inference for posterior approximation. Simulation results demonstrate that this algorithm can reach high value with compact policy representations, and stay computationally efficient when applying to agent set.
ContributorsSHIH, PO-KAN (Author) / Moraffah, Bahman (Thesis advisor) / Papandreou-Suppappola, Antonia (Thesis advisor) / Dasarathy, Gautam (Committee member) / Shih, YiChang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Most hardware today is based on von Neumann architecture separating memory from logic. Valuable processing time is lost in shuttling information back and forth between the two units, a problem called von Neumann bottleneck. As transistors are scaled further down, this bottleneck will make it harder to deliver performance in computing power. Adding to this is the increasing complexity of artificial intelligence logic. Thus, there is a need for a faster and more efficient method of computing. Neuromorphic systems deliver this by emulating the massively parallel and fault-tolerant computing capabilities of the human brain where the action potential is triggered by multiple inputs at once (spatial) or an input that builds up over time (temporal). Highly scalable memristors are key in these systems- they can maintain their internal resistive state based on previous current/voltage values thus mimicking the way the strength of two synapses in the brain can vary. The brain-inspired algorithms are implemented by vector matrix multiplications (VMMs) to provide neuronal outputs. High-density conductive bridging random access memory (CBRAM) crossbar arrays (CBAs) can perform VMMs parallelly with ultra-low energy.This research explores a simple planarization technique that could be potentially extended to integrate front-end-of-line (FEOL) processing of complementary metal oxide semiconductor (CMOS) circuitry with back-end-of-line (BEOL) processing of CBRAM CBAs for one-transistor one-resistor (1T1R) Neuromorphic CMOS chips where the transistor is part of the CMOS circuitry and the CBRAM forms the resistor. It is a photoresist (PR) and spin-on glass (SOG) based planarization recipe to planarize CBRAM electrode patterns on a silicon substrate. In this research, however, the planarization is only applied to mechanical grade (MG) silicon wafers without any CMOS layers on them. The planarization achieved was of a very high order (few tens of nanometers). Additionally, the recipe is cost-effective, provides good quality films and simple as only two types of process technologies are involved- lithography and dry etching.
Subsequent processing would involve depositing the CBRAM layers onto the planarized electrodes to form the resistor. Finally, the entire process flow is to be replicated onto wafers with CMOS layers to form the 1T1R circuit.
ContributorsBiswas, Prabaha (Author) / Barnaby, Hugh (Thesis advisor) / Kozicki, Michael (Committee member) / Velo, Yago Gonzalez (Committee member) / Arizona State University (Publisher)
Created2021
Description
Realization of efficient, high-bandgap photovoltaic cells produced using economically viable methods is a technological advance that could change the way we generate and use energy, and thereby accelerate the development of human civilization. There is a need to engineer a semiconductor material for solar cells, particularly multijunction cells, that has high (1.6-2.0 eV) bandgap, has relatively inactive defects, is thermodynamically stable under normal operating conditions with the potential for cost-effective thin-film growth in mass production.This work focuses on a material system made of gallium, indium, and phosphorus – the ternary semiconductor GaInP. GaInP based photovoltaic cells in single-crystal form have demonstrated excellent power conversion efficiency, however, growth of single-crystal GaInP is prohibitively expensive. While growth of polycrystalline GaInP is expected to lower production costs, polycrystalline GaInP is also expected to have a high density of electronically active defects, about which little is reported in scientific literature. This work presents the first study of synthesis, and structural and optoelectronic characterization of polycrystalline GaInP thin films.
In addition, this work models the best performance of polycrystalline solar cells achievable with a given grain size with grain-boundary/surface recombination velocity as a variable parameter. The effects of defect characteristics at the surface and layer properties such as doping and thickness on interface recombination velocity are also modeled.
Recombination velocities at the free surface of single-crystal GaInP and after deposition of various dielectric layers on GaInP are determined experimentally using time-resolved photoluminescence decay measurements. In addition, experimental values of bulk lifetime and surface recombination velocity in well-passivated single crystal AlInP-GaInP based double heterostructures are also measured for comparison to polycrystalline material systems.
A novel passivation method – aluminum-assisted post-deposition treatment or Al-PDT – was developed which shows promise as a general passivation and material improvement technique for polycrystalline thin films. In the GaInP system, this aluminum post-deposition treatment has demonstrated improvement in the minority carrier lifetime to 44 ns at 80 K. During development of the passivation process, aluminum diffusivity in GaInP was measured using TEM-EDS line scans. Introduction, development, and refinement of this novel passivation mechanism in polycrystalline GaInP could initiate the development of a new family of passivation treatments, potentially improving the optoelectronic response of other polycrystalline compound semiconductors as well.
ContributorsChikhalkar, Abhinav (Author) / King, Richard R (Thesis advisor) / Honsberg, Christiana (Committee member) / Newman, Nathan (Committee member) / Tongay, Sefaattin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Unlike conventional solar cells, modern high efficiency passivated contacts solar cells like silicon heterojunction (SHJ) cells have excellent surface passivation and use high bulk lifetime wafers which increase the operating injection level of these devices. These solar cell architectures can benefit from having lower doped substrates, with undoped solar cells becoming an attractive option. There has been very limited literature on high bulk resistivity substrates (>>10 Ωcm). This thesis work provides a comprehensive assessment of the potential of high resistivity/undoped substrates for high performance and more reliable silicon solar cells by demonstrating the results from modeling as well as characterization of SHJ solar cells fabricated with high resistivity/undoped substrates under real-world illumination and temperature conditions that the cells/modules experience in the field. In this work, the results from the analytical model demonstrated the effects of various defects, variation in doping and temperature on the performance of silicon solar cells. Experimentally, SHJ cells with bulk resistivities in the range of 1 Ωcm to >15k Ωcm were fabricated, and cell efficiencies over 20% were measured at standard testing conditions (STC) across the entire range of bulk resistivities. The illumination response (0.1-1 sun) and temperature coefficients (25-90 °C) were shown to be independent of the bulk resistivity. No light induced degradation was observed in the n-type SHJ cells of all resistivity ranges whereas high resistivity p-type SHJ cells showed less degradation compared to that of commercial resistivity range (<10 Ωcm). Very high reverse breakdown voltages (over 1 kV) were demonstrated for SHJ cells fabricated with high resistivity wafers. Using simulation, the importance of having cells in the modules with breakdown voltage higher than the series string voltage for safe and reliable operation of the photovoltaic (PV) system was highlighted.
The ingot yield can be improved by moving towards high resistivity ranges to manufacture high efficiency reliable solar cells by utilizing the entire ingot and eliminating the need to adhere to narrow resistivity range. Thus, the novel findings from this work can have profound impact on ingot and module manufacturing resulting in significant cost savings as well as improvement in the system reliability.
ContributorsSrinivasa, Apoorva (Author) / Bowden, Stuart (Thesis advisor) / Honsberg, Christiana (Committee member) / King, Richard (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2021