Micro mirrors based coupling of light to multi-core fiber realizing in-fiber photonic neural network processor

Eyal Cohen, Dror Malka, Amir Shemer, Asaf Shahmoon, Michael London, Zeev Zalevsky

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

Abstract

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a DMD based approaches to realize energetically efficient light coupling into a multi-core fiber realizing a unique design for in-fiber optical neural networks. Neurons and synapses are realized as individual cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in Erbium-doped cores mimics synaptic interactions. In order to dynamically and efficiently couple light into the multi-core fiber a DMD based micro mirror device is used to perform proper beam shaping operation. The beam shaping reshapes the light into a large set of points in space matching the positions of the required cores in the entrance plane to the multi-core fiber.

Original languageEnglish
Title of host publicationEmerging Digital Micromirror Device Based Systems and Applications IX
EditorsMichael R. Douglass, Benjamin L. Lee
PublisherSPIE
ISBN (Electronic)9781510606753
DOIs
StatePublished - 2017
EventEmerging Digital Micromirror Device Based Systems and Applications IX - San Francisco, United States
Duration: 30 Jan 201731 Jan 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10117

Conference

ConferenceEmerging Digital Micromirror Device Based Systems and Applications IX
Country/TerritoryUnited States
CitySan Francisco
Period30/01/1731/01/17

Keywords

  • Neural networks
  • beam shaping
  • multi-core fiber
  • optical computing

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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