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

In this work, we demonstrate the potential of HfO₂-based memristors as artificial synapses capable of reproducing biologically plausible spike-timing-dependent plasticity (STDP). W/HfO₂/Ti/TiN devices were fabricated and characterized, exhibiting reliable bipolar resistive switching, stable enduranc...

Descripción completa

Detalles Bibliográficos
Autores: Shooshtar, Mostafa, Pahlavan, Saeideh, Serrano Gotarredona, María Teresa, Linares Barranco, Bernabé
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/179766
Acceso en línea:https://hdl.handle.net/11441/179766
https://doi.org/10.1088/2634-4386/ae1da1
Access Level:acceso abierto
Palabra clave:HfO₂ memristor
Spike-timing-dependent plasticity (STDP)
Neuromorphic computing
Synaptic plasticity
Resistive switching
Artificial synapse
id ES_ae794cbb18febfeb8fc7bb30ccff43d3
oai_identifier_str oai:idus.us.es:11441/179766
network_acronym_str ES
network_name_str España
spelling Spike-timing-dependent plasticity and synaptic consolidation in Hfo₂ memristors for adaptive neuromorphic computing Shooshtar, Mostafa Pahlavan, Saeideh Serrano Gotarredona, María Teresa Linares Barranco, Bernabé HfO₂ memristor Spike-timing-dependent plasticity (STDP) Neuromorphic computing Synaptic plasticity Resistive switching Artificial synapse In this work, we demonstrate the potential of HfO₂-based memristors as artificial synapses capable of reproducing biologically plausible spike-timing-dependent plasticity (STDP). W/HfO₂/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 withexperimentalobservations in neuroscience. These findings highlight HfO₂ 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. IOP Publishing https://hdl.handle.net/11441/179766 https://doi.org/10.1088/2634-4386/ae1da1
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
Shooshtar, Mostafa
HfO₂ memristor
Spike-timing-dependent plasticity (STDP)
Neuromorphic computing
Synaptic plasticity
Resistive switching
Artificial synapse
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
author Shooshtar, Mostafa
author_facet Shooshtar, Mostafa
Pahlavan, Saeideh
Serrano Gotarredona, María Teresa
Linares Barranco, Bernabé
author_role author
author2 Pahlavan, Saeideh
Serrano Gotarredona, María Teresa
Linares Barranco, Bernabé
author2_role author
author
author
topic HfO₂ memristor
Spike-timing-dependent plasticity (STDP)
Neuromorphic computing
Synaptic plasticity
Resistive switching
Artificial synapse
topic_facet HfO₂ memristor
Spike-timing-dependent plasticity (STDP)
Neuromorphic computing
Synaptic plasticity
Resistive switching
Artificial synapse
description In this work, we demonstrate the potential of HfO₂-based memristors as artificial synapses capable of reproducing biologically plausible spike-timing-dependent plasticity (STDP). W/HfO₂/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 withexperimentalobservations in neuroscience. These findings highlight HfO₂ 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
format article
status_str publishedVersion
url https://hdl.handle.net/11441/179766
https://doi.org/10.1088/2634-4386/ae1da1
eu_rights_str_mv openAccess
publisher IOP Publishing
institution Universidad de Sevilla (US)
collection idUS. Depósito de Investigación de la Universidad de Sevilla
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
instname_str Universidad de Sevilla (US)
_version_ 1878439339057217536
publishDateSort 2025
author_browse Linares Barranco, Bernabé
Pahlavan, Saeideh
Serrano Gotarredona, María Teresa
Shooshtar, Mostafa
publisherStr IOP Publishing
score 6.924472

Similares en LA Referencia