Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Computer generated planar holograms for optical neural network implementations

Open Access Open Access

Abstract

While there are several ways to implement the interconnections in an optical neural network, we have concentrated on the use of multifaceted planar holograms. Each facet or subhologram produces the synaptic connections from a single neuron output (optical emitter or modulator) to an array of neuron inputs (detectors). The hologram must accurately encode synaptic weights, have a high diffraction efficiency, utilize the least space–bandwidth product, and be fabricated by using standard electron-beam lithography and ion-etch technology. The various algorithms used to generate the interconnection hologram are discussed, and the algorithms are compared by their performance. The algorithms we have investigated for hologram generation include the cell-based techniques, error diffusion, the Gergchberg–Saxton process, simulated annealing, random-search error minimization, a hybrid of the Gergchberg–Saxton and random-search error minimization, and the genetic algorithm. The hybrid Gergchberg–Saxton/error minimization process has produced the highest interconnect accuracy and highest diffraction efficiency of the algorithms tested to date.

© 1992 Optical Society of America

PDF Article
More Like This
Space-variant optical intercorrects via multifaceted planar holograms

Arthur F. Gmitro and Paul E. Keller
THHH4 OSA Annual Meeting (FIO) 1988

Implementation of a Packed Data Format for Production of Computer Generated Holograms by E-beam Lithography

Daniel M. Newman, Robert W. Hawley, and Neal C. Gallagher
WA2 Difraction Optics: Design, Fabrication, and Applications (DO) 1992

Methods for the generation of volume holograms via computer

Paul E. Keller and Arthur F. Gmitro
FD5 OSA Annual Meeting (FIO) 1992

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.