Optically addressed spatial light modulators for photonic neural network implementations

Vladimir Semenov, Xavier Porte, Maxime Jacquot, Laurent Larger, Ibrahim Abdulhalim, Daniel Brunner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a novel implementation of autonomous photonic neural networks based on optically-addressed spatial light modulators (OASLMs). In our approach, the OASLM operates as a spatially non-uniform birefringent waveplate, the retardation of which nonlinearly depends on the incident light intensity. We develop a complete electrical and optical model of the device and investigate the optimal operational characteristics. We study both, feed-forward and recurrent neural networks and demonstrate that OASLMs are promising candidates for the implement of autonomous photonic neural networks with large numbers of neurons and ultra low energy consumption.
Original languageEnglish
Title of host publicationEmerging Topics in Artificial Intelligence 2020
PublisherSPIE
Number of pages1
Volume11469
DOIs
StatePublished - 2020

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