Deep learning with synthetic photonic lattices for equalization in optical transmission systems

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Abstract

In this work we propose a new physical realization of optical neural network (ONN) based on a recently appeared technological platform of synthetic photonic lattices (SPL), and demonstrate its capabilities for deep learning. The system operates with time series of optical pulses with ability to easily control their parameters and possesses the architecture that well suits the ONN paradigm. We have also shown that such an ONN can be potentially utilized for signal processing in optical communication lines for signal distortion compensation.

Original languageEnglish
Title of host publicationReal-Time Photonic Measurements, Data Management, and Processing IV
EditorsMing Li, Bahram Jalali, Mohammad Hossein Asghari
PublisherSPIE
Number of pages11
ISBN (Electronic)9781510631014
DOIs
Publication statusPublished - 20 Nov 2019
EventReal-Time Photonic Measurements, Data Management, and Processing IV 2019 - Hangzhou, China
Duration: 22 Oct 201923 Oct 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11192
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceReal-Time Photonic Measurements, Data Management, and Processing IV 2019
CountryChina
CityHangzhou
Period22.10.201923.10.2019

Keywords

  • Deep learning
  • Optical Transmission Systems
  • Synthetic Photonic Lattices

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