A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing.

A large effort is devoted to the research of new computing paradigms associated with innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS (complementary metal oxide semiconductor) association. Among various propositions, spiking ne...

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Detalhes bibliográficos
Autores: Alibart, Fabien, Pleutin, Stéphane, Bichler, Olivier, Gamrat, Christian, Serrano Gotarredona, María Teresa, Linares Barranco, Bernabé, Vuillaume, Dominique
Formato: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2011
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/101600
Acesso em linha:https://hdl.handle.net/11441/101600
https://doi.org/10.1002/adfm.201101935
Access Level:acceso abierto
Palavra-chave:Organic electronics
Hybrid materials
Memristor
Neuromorphic device
Synaptic plasticity
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spelling A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing.Alibart, FabienPleutin, StéphaneBichler, OlivierGamrat, ChristianSerrano Gotarredona, María TeresaLinares Barranco, BernabéVuillaume, DominiqueOrganic electronicsHybrid materialsMemristorNeuromorphic deviceSynaptic plasticityA large effort is devoted to the research of new computing paradigms associated with innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS (complementary metal oxide semiconductor) association. Among various propositions, spiking neural network (SNN) seems a valid candidate. i) In terms of functions, SNN using relative spike timing for information coding are deemed to be the most effective at taking inspiration from the brain to allow fast and efficient processing of information for complex tasks in recognition or classification. ii) In terms of technology, SNN may be able to benefit the most from nanodevices because SNN architectures are intrinsically tolerant to defective devices and performance variability. Here, spike‐timing‐dependent plasticity (STDP), a basic and primordial learning function in the brain, is demonstrated with a new class of synapstor (synapse‐transistor), called nanoparticle organic memory field‐effect transistor (NOMFET). This learning function is obtained with a simple hybrid material made of the self‐assembly of gold nanoparticles and organic semiconductor thin films. Beyond mimicking biological synapses, it is also demonstrated how the shape of the applied spikes can tailor the STDP learning function. Moreover, the experiments and modeling show that this synapstor is a memristive device. Finally, these synapstors are successfully coupled with a CMOS platform emulating the pre‐ and postsynaptic neurons, and a behavioral macromodel is developed on usual device simulator.European Union FP7-216777WileyArquitectura y Tecnología de ComputadoresEuropean Union (UE)2011info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/101600https://doi.org/10.1002/adfm.201101935reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésAdvanced Functional Materials, 22 (3), 609-616.FP7-216777https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.201101935info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1016002026-06-17T12:51:07Z
dc.title.none.fl_str_mv A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing.
title A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing.
spellingShingle A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing.
Alibart, Fabien
Organic electronics
Hybrid materials
Memristor
Neuromorphic device
Synaptic plasticity
title_short A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing.
title_full A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing.
title_fullStr A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing.
title_full_unstemmed A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing.
title_sort A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing.
dc.creator.none.fl_str_mv Alibart, Fabien
Pleutin, Stéphane
Bichler, Olivier
Gamrat, Christian
Serrano Gotarredona, María Teresa
Linares Barranco, Bernabé
Vuillaume, Dominique
author Alibart, Fabien
author_facet Alibart, Fabien
Pleutin, Stéphane
Bichler, Olivier
Gamrat, Christian
Serrano Gotarredona, María Teresa
Linares Barranco, Bernabé
Vuillaume, Dominique
author_role author
author2 Pleutin, Stéphane
Bichler, Olivier
Gamrat, Christian
Serrano Gotarredona, María Teresa
Linares Barranco, Bernabé
Vuillaume, Dominique
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
European Union (UE)
dc.subject.none.fl_str_mv Organic electronics
Hybrid materials
Memristor
Neuromorphic device
Synaptic plasticity
topic Organic electronics
Hybrid materials
Memristor
Neuromorphic device
Synaptic plasticity
description A large effort is devoted to the research of new computing paradigms associated with innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS (complementary metal oxide semiconductor) association. Among various propositions, spiking neural network (SNN) seems a valid candidate. i) In terms of functions, SNN using relative spike timing for information coding are deemed to be the most effective at taking inspiration from the brain to allow fast and efficient processing of information for complex tasks in recognition or classification. ii) In terms of technology, SNN may be able to benefit the most from nanodevices because SNN architectures are intrinsically tolerant to defective devices and performance variability. Here, spike‐timing‐dependent plasticity (STDP), a basic and primordial learning function in the brain, is demonstrated with a new class of synapstor (synapse‐transistor), called nanoparticle organic memory field‐effect transistor (NOMFET). This learning function is obtained with a simple hybrid material made of the self‐assembly of gold nanoparticles and organic semiconductor thin films. Beyond mimicking biological synapses, it is also demonstrated how the shape of the applied spikes can tailor the STDP learning function. Moreover, the experiments and modeling show that this synapstor is a memristive device. Finally, these synapstors are successfully coupled with a CMOS platform emulating the pre‐ and postsynaptic neurons, and a behavioral macromodel is developed on usual device simulator.
publishDate 2011
dc.date.none.fl_str_mv 2011
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/101600
https://doi.org/10.1002/adfm.201101935
url https://hdl.handle.net/11441/101600
https://doi.org/10.1002/adfm.201101935
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Advanced Functional Materials, 22 (3), 609-616.
FP7-216777
https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.201101935
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301629

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