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2021-10-21Zeitschriftenartikel DOI: 10.18452/24068
Reversible training of waveguide-based AND/OR gates for optically driven artificial neural networks using photochromic molecules
dc.contributor.authorRhim, Seon-Young
dc.contributor.authorLigorio, Giovanni
dc.contributor.authorHermerschmidt, Felix
dc.contributor.authorPätzel, Michael
dc.contributor.authorHerder, Martin
dc.contributor.authorHecht, Stefan
dc.contributor.authorList-Kratochvil, Emil J.W.
dc.date.accessioned2022-02-01T14:39:58Z
dc.date.available2022-02-01T14:39:58Z
dc.date.issued2021-10-21none
dc.date.updated2022-02-01T13:20:52Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/24701
dc.description.abstractArtificial neural networks (ANNs) are inspired by the biological nervous system. The high performance of such ANNs is achieved through the dynamic change of the synaptic weights by applying self-optimizing learning algorithms. Despite the simple operations for each single element in an ANN, a network with a huge number of simulated elements consumes lots of computing capacity using von Neumann computer architectures. To overcome this issue, neuromorphic devices facilitate the design of hardware ANNs that emulate the synaptic functions. Here we demonstrate the viability of such an approach using photonic waveguides in combination with a photochromic diarylethene (DAE) molecule. By positioning and irradiating DAE molecules on single waveguides, we modulate the intensity and thereby emulate the plasticity of the synaptic weights. To run the photonic device as an ANN we firstly characterize the modulation range and encode a learning procedure accordingly. As the proof of concept, we operate a y-shaped waveguide performing basic AND/OR logic gate functions, with the capability to switch between these two gate functions by using specific training sets.eng
dc.description.sponsorshipDeutsche Forschungsgemeinschafthttp://dx.doi.org/10.13039/501100001659
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectartificial neural networkseng
dc.subjectbackpropagationeng
dc.subjectgradient descenteng
dc.subjectneuromorphic deviceseng
dc.subjectphotochromic moleculeseng
dc.subjectsynaptic plasticityeng
dc.subjectwaveguideseng
dc.subject.ddc530 Physiknone
dc.titleReversible training of waveguide-based AND/OR gates for optically driven artificial neural networks using photochromic moleculesnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/24701-7
dc.identifier.doihttp://dx.doi.org/10.18452/24068
dc.type.versionpublishedVersionnone
local.edoc.pages10none
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
dc.description.versionPeer Reviewednone
dc.identifier.eissn1361-6463
dcterms.bibliographicCitation.doi10.1088/1361-6463/ac2d62none
dcterms.bibliographicCitation.journaltitleJournal of physicsnone
dcterms.bibliographicCitation.volume55none
dcterms.bibliographicCitation.issue4none
dcterms.bibliographicCitation.articlenumber044002none
dcterms.bibliographicCitation.originalpublishernameIOP Publishingnone
dcterms.bibliographicCitation.originalpublisherplaceBristolnone
bua.departmentMathematisch-Naturwissenschaftliche Fakultätnone

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