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.

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

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