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

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dc.contributor.authorPankov, A
dc.contributor.authorSidelnikov, Oleg S.
dc.contributor.authorVatnik, I D
dc.contributor.authorSukhorukov, Andrey
dc.contributor.authorChurkin, D V
dc.contributor.editorLi, M
dc.contributor.editorJalali, B
dc.contributor.editorAsghari, M H
dc.coverage.spatialHangzhou, China
dc.date.accessioned2023-03-21T22:23:39Z
dc.date.available2023-03-21T22:23:39Z
dc.date.createdOct 20-23 2019
dc.date.issued2019
dc.date.updated2022-01-16T07:18:18Z
dc.description.abstractIn 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),1, 2 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.en_AU
dc.description.sponsorshipThis work is supported by Ministry of Education and Science of the Russian Federation (3.7672.2017/8.9)en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.isbn9781510631014en_AU
dc.identifier.issn0277-786Xen_AU
dc.identifier.urihttp://hdl.handle.net/1885/287253
dc.language.isoen_AUen_AU
dc.provenancehttps://v2.sherpa.ac.uk/id/publication/27454..."The Published Version can be archived in Institutional Repository" from SHERPA/RoMEO site (as at 22/03/2023). Copyright 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. Artem V. Pankov, Oleg S. Sidelnikov, Ilya D. Vatnik, Andrey A. Sukhorukov, Dmitriy V. Churkin, "Deep learning with synthetic photonic lattices for equalization in optical transmission systems," Proc. SPIE 11192, Real-time Photonic Measurements, Data Management, and Processing IV, 111920N (20 November 2019); doi: 10.1117/12.2537462en_AU
dc.publisherSPIEen_AU
dc.relation.ispartofseriesSPIE/COS Photonics Asia, 2019en_AU
dc.rights© 2019 SPIEen_AU
dc.sourceReal-time Photonic Measurements, Data Management, and Processing IVen_AU
dc.titleDeep learning with synthetic photonic lattices for equalization in optical transmission systemsen_AU
dc.typeConference paperen_AU
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.lastpage111920N-2en_AU
local.bibliographicCitation.startpage111920N-1en_AU
local.contributor.affiliationPankov, A, Novosibirsk State Universityen_AU
local.contributor.affiliationSidelnikov, Oleg S., Novosibirsk State Universityen_AU
local.contributor.affiliationVatnik, I D, Novosibirsk State Universityen_AU
local.contributor.affiliationSukhorukov, Andrey, College of Science, ANUen_AU
local.contributor.affiliationChurkin, D V, Novosibirsk State Universityen_AU
local.contributor.authoruidSukhorukov, Andrey, u9810122en_AU
local.description.notesImported from ARIESen_AU
local.description.refereedYes
local.identifier.absfor510200 - Atomic, molecular and optical physicsen_AU
local.identifier.absfor510800 - Quantum physicsen_AU
local.identifier.ariespublicationa383154xPUB11824en_AU
local.identifier.doi10.1117/12.2537462en_AU
local.identifier.essn1996-756Xen_AU
local.identifier.scopusID2-s2.0-85078327501
local.publisher.urlhttps://www.spiedigitallibrary.org/en_AU
local.type.statusPublished Versionen_AU

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