ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Electrical Engineering
- Creators: Allee, David R.
An important part of this elaborate project is the development of a new load forecasting algorithm and a better control strategy for the optimized utilization of the storage system. The built-in algorithm of this commercial unit uses simple forecasting and battery control strategies. With the recent improvement in Machine Learning (ML) techniques, development of a more sophisticated model of the problem in hand was possible. This research is aimed at achieving the goal by utilizing the appropriate ML techniques to better model the problem, which will essentially result in a better solution. In this research, a set of six unique features are used to model the load forecasting problem and different ML algorithms are simulated on the developed model. A similar approach is taken to solve the PV prediction problem. Finally, a very effective battery control strategy is built (utilizing the results of the load and PV forecasting), with the aim of ensuring a reduction in the amount of energy consumed from the grid during the “on-peak” hours. Apart from the reduction in the energy consumption, this battery control algorithm decelerates the “cycling aging” or the aging of the battery owing to the charge/dis-charges cycles endured by selectively charging/dis-charging the battery based on need.
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The results of this proposed strategy are verified using a hardware implementation (the PV system was coupled with a custom-built load bank and this setup was used to simulate a house). The results pertaining to the performances of the built-in algorithm and the ML algorithm are compared and the economic analysis is performed. The findings of this research have in the process of being published in a reputed journal.
The simulations consist of no radiation and radiation modeling. The no radiation modeling details the cell structure development and characterizes basic operations (read, erase and program) of a flash memory cell. The program time is observed to be approximately 10 μs while the erase time is approximately 0.1 ms.
The radiation modeling uses the fixed oxide charge method to analyze the TID effects on the same flash memory cell. After irradiation, a threshold voltage shift of the flash memory cell is observed. The threshold voltages of a programmed cell and an erased cell are reduced at an average rate of 0.025 V/krad.
The use of simulation techniques allows designers to better understand the TID response of a SST flash memory cell and to predict cell level TID effects without performing the costly in-situ irradiation experiments. The simulation and experimental results agree qualitatively. In particular, simulation results reveal that ‘0’ to ‘1’ errors but not ‘1’ to ‘0’ retention errors occur; likewise, ‘0’ to ‘1’ errors dominate experimental testing, which also includes circuitry effects that can cause ‘1’ to ‘0’ failures. Both simulation and experimental results reveal flash memory cell TID resilience to about 200 krad.
Mixed oxide semiconductor TFTs on flexible plastic substrates suffer from performance and stability issues related to the maximum processing temperature limitation of the polymer. A novel device architecture based upon a dual active layer improves both the performance and stability. Devices are directly fabricated below 200 ºC on a polyethylene naphthalate (PEN) substrate using mixed metal oxides of either zinc indium oxide (ZIO) or indium gallium zinc oxide (IGZO) as the active semiconductor. The dual active layer architecture allows for adjustment to the saturation mobility and threshold voltage stability without the requirement of high temperature annealing, which is not compatible with flexible plastic substrates like PEN. The device performance and stability is strongly dependent upon the composition of the mixed metal oxide; this dependency provides a simple route to improving the threshold voltage stability and drive performance. By switching from a single to a dual active layer, the saturation mobility increases from 1.2 cm2/V-s to 18.0 cm2/V-s, while the rate of the threshold voltage shift decreases by an order of magnitude. This approach could assist in enabling the production of devices on flexible substrates using amorphous oxide semiconductors.
Low temperature (200°C) processed amorphous silicon photodiodes were developed successfully by balancing the tradeoffs between low temperature and low stress (less than -70 MPa compressive) and device performance. Devices with a dark current of less than 1.0 pA/mm2 and a quantum efficiency of 68% have been demonstrated. Alternative processing techniques, such as pixelating the PIN diode and using organic photodiodes have also been explored for applications where extreme flexibility is desired.
This thesis details the results of an effort to create a first-principles MATLAB simulation model that uses configurable physical parameters to generate images of dendritic structures. Generated images are compared against real-world samples. While growth has a significant random component, there are several reliable characteristics that form under similar parameter sets that can be monitored such as the relative length of major dendrite arms, common branching angles, and overall growth directionality.
The first simulation model that was constructed takes a Newtonian perspective of the problem and is implemented using the Euler numerical method. This model has several shortcomings stemming majorly from the simplistic treatment of the problem, but is highly performant. The model is then revised to use the Verlet numerical method, which increases the simulation accuracy, but still does not fully resolve the issues with the theoretical background. The final simulation model returns to the Euler method, but is a stochastic model based on Mott-Gurney’s ion hopping theory applied to solids. The results from this model are seen to match real samples the closest of all simulations.
Digital systems are important to the technological improvements in space exploration. Space exploration requires radiation hardened microprocessors which minimize the functional disruptions in the presence of radiation. The design highly efficient radiation-hardened microprocessor for enabling spacecraft (HERMES) is a radiation-hardened microprocessor with performance comparable to the commercially available designs. These designs are manufactured using a foundry complementary metal-oxide semiconductor (CMOS) 55-nm triple-well process. This thesis presents the post silicon validation results of the HERMES and the PUF mode of SRAM across process corners.
Chapter 1 gives an overview of the blocks implemented on the test chip 25. It also talks about the pre-silicon functional verification methodology used for the test chip. Chapter 2 discusses about the post silicon testing setup of test chip 25 and the validation of the setup. Chapter 3 describes the architecture and the test bench of the HERMES along with its testing results. Chapter 4 discusses the test bench and the perl scripts used to test the SRAM along with its testing results. Chapter 5 gives a summary of the post-silicon validation results of the HERMES and the PUF mode of SRAM.