Publication Date

5-2013

Date of Final Oral Examination (Defense)

12-12-2012

Type of Culminating Activity

Thesis

Degree Title

Master of Science in Electrical Engineering

Department

Electrical and Computer Engineering

Supervisory Committee Chair

Elisa H. Barney Smith, Ph.D.

Supervisory Committee Member

Kristy Campbell, Ph.D.

Supervisory Committee Member

Vishal Saxena, Ph.D.

Abstract

Artificial neural networks have recently received renewed interest because of the discovery of the memristor. The memristor is the fourth basic circuit element, hypothesized to exist by Leon Chua in 1971 and physically realized in 2008. The two-terminal device acts like a resistor with memory and is therefore of great interest for use as a synapse in hardware ANNs. Recent advances in memristor technology allowed these devices to migrate from the experimental stage to the application stage.

This Master's thesis presents the development of a threshold logic gate (TLG), which is a special case of an ANN, implemented with discrete circuit elements using memristors as synapses. Further, a programming circuit is developed, allowing the memristors and therefore the network to be reconfigured and trained in real-time. The results show that memristors are indeed viable for use in ANNs, but are somewhat hard to control as a lot of intrinsic device characteristics are still under investigation and are currently not fully understood. A simple threshold logic gate was built and can be reconfigured to implement AND, OR, NAND, and NOR functionality. The findings presented here contribute towards improvements on the device as well as algorithmic level to implement a memristor-based ANN capable of on-line learning.

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