Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks

Detalhes bibliográficos
Autor(a) principal: Pereira, Maria Elias
Data de Publicação: 2022
Outros Autores: Deuermeier, Jonas, Freitas, Pedro, Barquinha, Pedro, Zhang, Weidong, Martins, Rodrigo, Fortunato, Elvira, Kiazadeh, Asal
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/134350
Resumo: UIDB/50025/2020-202 DFA/BD/8335/2020 No. PTDC/NAN-MAT/30812/2017 Grant Nos. EP/M006727/1 EP/S000259/1
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spelling Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networksMaterials Science(all)Engineering(all)UIDB/50025/2020-202 DFA/BD/8335/2020 No. PTDC/NAN-MAT/30812/2017 Grant Nos. EP/M006727/1 EP/S000259/1Neuromorphic computation based on resistive switching devices represents a relevant hardware alternative for artificial deep neural networks. For the highest accuracies on pattern recognition tasks, an analog, linear, and symmetric synaptic weight is essential. Moreover, the resistive switching devices should be integrated with the supporting electronics, such as thin-film transistors (TFTs), to solve crosstalk issues on the crossbar arrays. Here, an a-Indium-gallium-zinc-oxide (IGZO) memristor is proposed, with Mo and Ti/Mo as bottom and top contacts, with forming-free analog switching ability for an upcoming integration on crossbar arrays with a-IGZO TFTs for neuromorphic hardware systems. The development of a TFT compatible fabrication process is accomplished, which results in an a-IGZO memristor with a high stability and low cycle-to-cycle variability. The synaptic behavior through potentiation and depression tests using an identical spiking scheme is presented, and the modulation of the plasticity characteristics by applying non-identical spiking schemes is also demonstrated. The pattern recognition accuracy, using MNIST handwritten digits dataset, reveals a maximum of 91.82% accuracy, which is a promising result for crossbar implementation. The results displayed here reveal the potential of Mo/a-IGZO/Ti/Mo memristors for neuromorphic hardware.DCM - Departamento de Ciência dos MateriaisCENIMAT-i3N - Centro de Investigação de Materiais (Lab. Associado I3N)UNINOVA-Instituto de Desenvolvimento de Novas TecnologiasRUNPereira, Maria EliasDeuermeier, JonasFreitas, PedroBarquinha, PedroZhang, WeidongMartins, RodrigoFortunato, ElviraKiazadeh, Asal2022-03-11T23:22:49Z2022-01-012022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/134350eng2166-532XPURE: 41739352https://doi.org/10.1063/5.0073056info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:12:49Zoai:run.unl.pt:10362/134350Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:04.548910Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks
title Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks
spellingShingle Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks
Pereira, Maria Elias
Materials Science(all)
Engineering(all)
title_short Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks
title_full Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks
title_fullStr Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks
title_full_unstemmed Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks
title_sort Tailoring the synaptic properties of a-IGZO memristors for artificial deep neural networks
author Pereira, Maria Elias
author_facet Pereira, Maria Elias
Deuermeier, Jonas
Freitas, Pedro
Barquinha, Pedro
Zhang, Weidong
Martins, Rodrigo
Fortunato, Elvira
Kiazadeh, Asal
author_role author
author2 Deuermeier, Jonas
Freitas, Pedro
Barquinha, Pedro
Zhang, Weidong
Martins, Rodrigo
Fortunato, Elvira
Kiazadeh, Asal
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv DCM - Departamento de Ciência dos Materiais
CENIMAT-i3N - Centro de Investigação de Materiais (Lab. Associado I3N)
UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
RUN
dc.contributor.author.fl_str_mv Pereira, Maria Elias
Deuermeier, Jonas
Freitas, Pedro
Barquinha, Pedro
Zhang, Weidong
Martins, Rodrigo
Fortunato, Elvira
Kiazadeh, Asal
dc.subject.por.fl_str_mv Materials Science(all)
Engineering(all)
topic Materials Science(all)
Engineering(all)
description UIDB/50025/2020-202 DFA/BD/8335/2020 No. PTDC/NAN-MAT/30812/2017 Grant Nos. EP/M006727/1 EP/S000259/1
publishDate 2022
dc.date.none.fl_str_mv 2022-03-11T23:22:49Z
2022-01-01
2022-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/134350
url http://hdl.handle.net/10362/134350
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2166-532X
PURE: 41739352
https://doi.org/10.1063/5.0073056
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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