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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.


Machine learning technology has made a lot of incredible achievements in recent years. It has rivalled or exceeded human performance in many intellectual tasks including image recognition, face detection and the Go game. Many machine learning algorithms require huge amount of computation such as in multiplication of large matrices. As silicon technology has scaled to sub-14nm regime, simply scaling down the device cannot provide enough speed-up any more. New device technologies and system architectures are needed to improve the computing capacity. Designing specific hardware for machine learning is highly in demand. Efforts need to be made on a joint design …

Contributors
Xu, Zihan, Cao, Yu, Chakrabarti, Chaitali, et al.
Created Date
2017

The Resistive Random Access Memory (ReRAM) is an emerging non-volatile memory technology because of its attractive attributes, including excellent scalability (< 10 nm), low programming voltage (< 3 V), fast switching speed (< 10 ns), high OFF/ON ratio (> 10), good endurance (up to 1012 cycles) and great compatibility with silicon CMOS technology [1]. However, ReRAM suffers from larger write latency, energy and reliability issue compared to Dynamic Random Access Memory (DRAM). To improve the energy-efficiency, latency efficiency and reliability of ReRAM storage systems, a low cost cross-layer approach that spans device, circuit, architecture and system levels is proposed. For …

Contributors
Mao, Manqing, Chakrabariti, Chaitali, Yu, Shimeng, et al.
Created Date
2019

Over the past few decades, the silicon complementary-metal-oxide-semiconductor (CMOS) technology has been greatly scaled down to achieve higher performance, density and lower power consumption. As the device dimension is approaching its fundamental physical limit, there is an increasing demand for exploration of emerging devices with distinct operating principles from conventional CMOS. In recent years, many efforts have been devoted in the research of next-generation emerging non-volatile memory (eNVM) technologies, such as resistive random access memory (RRAM) and phase change memory (PCM), to replace conventional digital memories (e.g. SRAM) for implementation of synapses in large-scale neuromorphic computing systems. Essentially being compact …

Contributors
Chen, Pai-Yu, Yu, Shimeng, Cao, Yu, et al.
Created Date
2018

The aging process due to Bias Temperature Instability (both NBTI and PBTI) and Channel Hot Carrier (CHC) is a key limiting factor of circuit lifetime in CMOS design. Threshold voltage shift due to BTI is a strong function of stress voltage and temperature complicating stress and recovery prediction. This poses a unique challenge for long-term aging prediction for wide range of stress patterns. Traditional approaches usually resort to an average stress waveform to simplify the lifetime prediction. They are efficient, but fail to capture circuit operation, especially under dynamic voltage scaling (DVS) or in analog/mixed signal designs where the stress …

Contributors
Sutaria, Ketul, Cao, Yu, Bakkaloglu, Bertan, et al.
Created Date
2014

Internet of Things (IoT) has become a popular topic in industry over the recent years, which describes an ecosystem of internet-connected devices or things that enrich the everyday life by improving our productivity and efficiency. The primary components of the IoT ecosystem are hardware, software and services. While the software and services of IoT system focus on data collection and processing to make decisions, the underlying hardware is responsible for sensing the information, preprocess and transmit it to the servers. Since the IoT ecosystem is still in infancy, there is a great need for rapid prototyping platforms that would help …

Contributors
Suda, Naveen, Cao, Yu, Bakkaloglu, Bertan, et al.
Created Date
2016

The recent flurry of security breaches have raised serious concerns about the security of data communication and storage. A promising way to enhance the security of the system is through physical root of trust, such as, through use of physical unclonable functions (PUF). PUF leverages the inherent randomness in physical systems to provide device specific authentication and encryption. In this thesis, first the design of a highly reliable resistive random access memory (RRAM) PUF is presented. Compared to existing 1 cell/bit RRAM, here the sum of the read-out currents of multiple RRAM cells are used for generating one response bit. …

Contributors
Shrivastava, Ayush, Chakrabarti, Chaitali, Yu, Shimeng, et al.
Created Date
2015

Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for radiation environment, hardware security system and machine learning applications. In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, …

Contributors
LIU, RUI, Yu, Shimeng, Yu, Shimeng, et al.
Created Date
2018