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9 August 1996 Optical neural networks using electron trapping materials
Alastair D. McAulay, Junqing Wang
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Proceedings Volume 10287, Optoelectronic Devices and Systems for Processing: A Critical Review; 102870E (1996) https://doi.org/10.1117/12.259692
Event: SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, 1996, Denver, CO, United States
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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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Alastair D. McAulay and Junqing Wang "Optical neural networks using electron trapping materials", Proc. SPIE 10287, Optoelectronic Devices and Systems for Processing: A Critical Review, 102870E (9 August 1996); https://doi.org/10.1117/12.259692
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KEYWORDS
Neural networks
Content addressable memory
Detection and tracking algorithms
Machine learning
Sensors
Systems modeling
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