Spike-timing-dependent plasticity and synaptic consolidation in Hfo2 memristors for adaptive neuromorphic computing

In this work, we demonstrate the potential of HfO<inf>2</inf>-based memristors as artificial synapses capable of reproducing biologically plausible spike-timing-dependent plasticity (STDP). W/HfO<inf>2</inf>/Ti/TiN devices were fabricated and characterized, exhibiting reliabl...

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Detalles Bibliográficos
Autores: Shooshtari, Mostafa, Pahlavan, Saeideh, Serrano-Gotarredona, Teresa, Linares-Barranco, Bernabé
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/410046
Acceso en línea:http://hdl.handle.net/10261/410046
https://api.elsevier.com/content/abstract/scopus_id/105022695011
Access Level:acceso abierto
Palabra clave:Artificial synapse
HfO2 memristor
Neuromorphic computing
Resistive switching
Spike-timing-dependent plasticity (STDP)
Synaptic plasticity
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spelling Spike-timing-dependent plasticity and synaptic consolidation in Hfo2 memristors for adaptive neuromorphic computingShooshtari, MostafaPahlavan, SaeidehSerrano-Gotarredona, TeresaLinares-Barranco, BernabéArtificial synapseHfO2 memristorNeuromorphic computingResistive switchingSpike-timing-dependent plasticity (STDP)Synaptic plasticityIn this work, we demonstrate the potential of HfO<inf>2</inf>-based memristors as artificial synapses capable of reproducing biologically plausible spike-timing-dependent plasticity (STDP). W/HfO<inf>2</inf>/Ti/TiN devices were fabricated and characterized, exhibiting reliable bipolar resistive switching, stable endurance, and reproducible resistance states across multiple cells and devices. The excitatory postsynaptic current (EPSC) response under sequential voltage pulses revealed gradual potentiation, depression, and saturation dynamics, closely resembling long-term potentiation, long-term depression, and synaptic consolidation in biological systems. Furthermore, the memristors successfully emulated higher-order learning rules, including triplet-STDP and frequency-dependent plasticity, while maintaining robust performance under biologically realistic noise conditions, exhibiting less than ±2% variation under voltage perturbations and ±2.5% under spike-timing jitter across 25 trials. A compact physical model captured the interplay between vacancy-driven filament dynamics and time-dependent weight modulation, yielding STDP curves consistent with experimental observations in neuroscience. These findings highlight HfO<inf>2</inf> memristors as promising candidates for neuromorphic computing, providing not only a faithful hardware realization of synaptic learning but also compatibility with large-scale, CMOS-integrated architectures for next-generation cognitive processors.This work has been funded in part by EU Grant 101070908 (CROSSBRAIN), by grant PID2023-149071NB-C51 (EUPHORIC, funded by the Ministry of Science, Innovation, and Universities), and by Grant TSI-069100-2023-001 (USECHIP, project funded by the Secretary of State for Telecommunications and Digital Infrastructure, Ministry for Digital Transformation and Civil Service and by the European Union–NextGenerationEU/PRTR).Peer reviewedIOP PublishingEuropean CommissionMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Ministerio para la Transformación Digital y de la Función Pública (España)Shooshtari, Mostafa [0000-0003-1292-0683]Pahlavan, Saeideh [0000-0002-1841-6069]Linares-Barranco, Bernabé [0000-0002-1813-4889]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/410046https://api.elsevier.com/content/abstract/scopus_id/105022695011reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/HE/101070908info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-149071NB-C51Shooshtari, Mostafa; Pahlavan, Saeideh; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé; 2025; Supplementary Data: Spike-Timing-Dependent Plasticity and Synaptic Consolidation in HfO₂ Memristors for Adaptive Neuromorphic Computing; IOP Publishing; https://doi.org/10.1088/2634-4386/ae1da1/data1https://doi.org/10.1088/2634-4386/ae1da1Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4100462026-05-22T06:33:51Z
dc.title.none.fl_str_mv Spike-timing-dependent plasticity and synaptic consolidation in Hfo2 memristors for adaptive neuromorphic computing
title Spike-timing-dependent plasticity and synaptic consolidation in Hfo2 memristors for adaptive neuromorphic computing
spellingShingle Spike-timing-dependent plasticity and synaptic consolidation in Hfo2 memristors for adaptive neuromorphic computing
Shooshtari, Mostafa
Artificial synapse
HfO2 memristor
Neuromorphic computing
Resistive switching
Spike-timing-dependent plasticity (STDP)
Synaptic plasticity
title_short Spike-timing-dependent plasticity and synaptic consolidation in Hfo2 memristors for adaptive neuromorphic computing
title_full Spike-timing-dependent plasticity and synaptic consolidation in Hfo2 memristors for adaptive neuromorphic computing
title_fullStr Spike-timing-dependent plasticity and synaptic consolidation in Hfo2 memristors for adaptive neuromorphic computing
title_full_unstemmed Spike-timing-dependent plasticity and synaptic consolidation in Hfo2 memristors for adaptive neuromorphic computing
title_sort Spike-timing-dependent plasticity and synaptic consolidation in Hfo2 memristors for adaptive neuromorphic computing
dc.creator.none.fl_str_mv Shooshtari, Mostafa
Pahlavan, Saeideh
Serrano-Gotarredona, Teresa
Linares-Barranco, Bernabé
author Shooshtari, Mostafa
author_facet Shooshtari, Mostafa
Pahlavan, Saeideh
Serrano-Gotarredona, Teresa
Linares-Barranco, Bernabé
author_role author
author2 Pahlavan, Saeideh
Serrano-Gotarredona, Teresa
Linares-Barranco, Bernabé
author2_role author
author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Ministerio para la Transformación Digital y de la Función Pública (España)
Shooshtari, Mostafa [0000-0003-1292-0683]
Pahlavan, Saeideh [0000-0002-1841-6069]
Linares-Barranco, Bernabé [0000-0002-1813-4889]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Artificial synapse
HfO2 memristor
Neuromorphic computing
Resistive switching
Spike-timing-dependent plasticity (STDP)
Synaptic plasticity
topic Artificial synapse
HfO2 memristor
Neuromorphic computing
Resistive switching
Spike-timing-dependent plasticity (STDP)
Synaptic plasticity
description In this work, we demonstrate the potential of HfO<inf>2</inf>-based memristors as artificial synapses capable of reproducing biologically plausible spike-timing-dependent plasticity (STDP). W/HfO<inf>2</inf>/Ti/TiN devices were fabricated and characterized, exhibiting reliable bipolar resistive switching, stable endurance, and reproducible resistance states across multiple cells and devices. The excitatory postsynaptic current (EPSC) response under sequential voltage pulses revealed gradual potentiation, depression, and saturation dynamics, closely resembling long-term potentiation, long-term depression, and synaptic consolidation in biological systems. Furthermore, the memristors successfully emulated higher-order learning rules, including triplet-STDP and frequency-dependent plasticity, while maintaining robust performance under biologically realistic noise conditions, exhibiting less than ±2% variation under voltage perturbations and ±2.5% under spike-timing jitter across 25 trials. A compact physical model captured the interplay between vacancy-driven filament dynamics and time-dependent weight modulation, yielding STDP curves consistent with experimental observations in neuroscience. These findings highlight HfO<inf>2</inf> memristors as promising candidates for neuromorphic computing, providing not only a faithful hardware realization of synaptic learning but also compatibility with large-scale, CMOS-integrated architectures for next-generation cognitive processors.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/410046
https://api.elsevier.com/content/abstract/scopus_id/105022695011
url http://hdl.handle.net/10261/410046
https://api.elsevier.com/content/abstract/scopus_id/105022695011
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/HE/101070908
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-149071NB-C51
Shooshtari, Mostafa; Pahlavan, Saeideh; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé; 2025; Supplementary Data: Spike-Timing-Dependent Plasticity and Synaptic Consolidation in HfO₂ Memristors for Adaptive Neuromorphic Computing; IOP Publishing; https://doi.org/10.1088/2634-4386/ae1da1/data1
https://doi.org/10.1088/2634-4386/ae1da1
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IOP Publishing
publisher.none.fl_str_mv IOP Publishing
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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