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Author ORCID Identifier

https://orcid.org/0000-0002-4043-6701

AccessType

Campus-Only Access for Five (5) Years

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Electrical and Computer Engineering

Year Degree Awarded

2021

Month Degree Awarded

May

First Advisor

Jianhua Joshua Yang

Subject Categories

Electronic Devices and Semiconductor Manufacturing

Abstract

Neuromorphic computing describes the use of electrical circuits to mimic biological architecture presented in nervous systems. Crossbar-structured artificial neural network is a promising emulator of biological neural network due to its high density, massive connectivity and efficient implementation of weighted sums.

Depending on the types of signals the artificial neural network is processing, it can be classified into non-spiking neural network and spiking neural network. In a typical working scheme of a crossbar non-spiking neural network, the electrical conductance of the cross-point device is used to represent the weight so that the total output current at the end of each column is a weighted sum of the input voltages to the rows. As a result, programming the conductance (weight) is critical for neural network training. Memristor is a two-terminal passive circuit element that exhibits a tunable conductance driven by input voltages. Due to its great performance in multilevel, speed, endurance, retention, stackability and scalability, the memristor has become one of the most promising candidates for future artificial synapse.

By analyzing and understanding the memristive behavior under electrical stimulations, we proposed an algorithm that can tune the device conductance precisely and enhance the stability of resistance levels by reducing the random telegraph noise. With the help of the algorithm, we have successfully demonstrated 2048 discrete conductance levels in memristors, which is the highest among all types of non-volatile resistive switching devices. We have also shown that the device can maintain its conductance within a very narrow fluctuation range for a long time after stabilization.

We also invented a capacitive switching device that processes spiking signals naturally with the leaky integrate-and-fire function similar to a biological spiking neuron. We further demonstrated spatial summation and signal propagation capability by integrating such neuron with a transistor to form a neuro-transistor. Finally, we built the first capacitive spiking neural networks, with which we have successfully demonstrated pattern classification with capacitive version of dot-product.

DOI

https://doi.org/10.7275/22100134.0

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Available for download on Saturday, May 14, 2022

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