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Proceedings Paper

Optical neural networks using electron trapping materials
Author(s): Alastair D. McAulay; Junqing Wang
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Paper Abstract

The characteristics of electron trapping optical material are reviewed and equations for modeling the behavior developed. Classical supervised and unsupervised learning algorithms, suitable for optical implementation, are discussed. The principles, optical set up, experimental laboratory results and limitations are described for three optical learning demonstration systems: a supervised Hebbian optical learning associative memory, a supervised Perceptron classifier, and an unsupervised Hebbian optical learning novelty detector. Results show the potential and limitations of electron trapping optical material for optical neural network systems that use optical learning.

Paper Details

Date Published: 9 August 1996
PDF: 26 pages
Proc. SPIE 10287, Optoelectronic Devices and Systems for Processing: A Critical Review, 102870E (9 August 1996); doi: 10.1117/12.259692
Show Author Affiliations
Alastair D. McAulay, Lehigh Univ. (United States)
Junqing Wang, Lehigh Univ. (United States)


Published in SPIE Proceedings Vol. 10287:
Optoelectronic Devices and Systems for Processing: A Critical Review
Bahram Javidi; Kristina M. Johnson, Editor(s)

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