Date of Award


Document Type

Masters Thesis

Degree Name


Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science

First Advisor

Richard M. Voyles, Ph.D.

Second Advisor

Sean E. Shaheen, Ph.D.

Third Advisor

Roger Salters

Fourth Advisor

Gao Wenzhong


Districuted computing, Hardware neural network, Neuromorphic engineering, Organic bistable devices, Organic electronics, Synthetic neural network


This thesis presents work done simulating a type of organic neuromorphic architecture, modeled after Artificial Neural Network, and termed Synthetic Neural Network, or SNN. The first major contribution of this thesis is development of a single-transistor-single-organic-bistable-device-per-input circuit that approximates behavior of an artificial neuron. The efficacy of this design is validated by comparing the behavior of a single synthetic neuron to that of an artificial neuron as well as two examples involving a network of synthetic neurons. The analysis utilizes electrical characteristics of polymer electronic elements, namely Organic Bistable Device and Organic Field Effect Transistor, created in the laboratory at University of Denver. Polymer electronics is a new branch of electronics that is based on conductive and semi-conductive polymers. These new elements hold a great advantage over the inorganic electronics in the form of physical flexibility and low cost of fabrication. However, their device variability between individual devices is also much greater. Therefore the second major contribution of this thesis is the analysis of resilience of neural networks subjected to physical damage and other manufacturing faults.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Robert A. Nawrocki


Received from ProQuest

File Format




File Size

125 p.


Computer Engineering, Engineering, Artificial Intelligence