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Proceedings Paper

Optoelectronic neuron arrays
Author(s): Demetri Psaltis; Steven H. Lin
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Paper Abstract

The optical implementation of a neural network consists of two basic components: a 2-D array of neurons and interconnections. Each neuron is a nonlinear processing element that, in its simplest form, produces an output which is the thresholded version of the input. Liquid crystal spatial light modulators, optoelectronic integrated circuits (OEIC's), either hybrid, such as liquid crystal on silicon, Si-PLZT, and flip-chip devices, or monolithic integration in 111-V compounds, are examples of such a solution. In order for these devices to be used as neurons in a practical experiment, they must contain a large number of neurons (104/cm2 - 106/cm2) and exhibit high gain. This puts a stringent requirement on the electrical power dissipation. Thus, these devices have to be operated at low enough current levels so that the power dissipation on the chip does not exceed the heat-sinking capability, and yet the current levels need to be large enough to be able to produce high gain. This means sensitive input devices are a must. To achieve these goals, the speed requirement of the devices must be relaxed as the operation of neural network does not have to be too fast.

Paper Details

Date Published: 1 December 1991
PDF: 9 pages
Proc. SPIE 1562, Devices for Optical Processing, (1 December 1991); doi: 10.1117/12.50779
Show Author Affiliations
Demetri Psaltis, California Institute of Technology (United States)
Steven H. Lin, California Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1562:
Devices for Optical Processing
Debra M. Gookin, Editor(s)

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