Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

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dc.contributor.authorZhu, Kaichencs
dc.contributor.authorMahmoodi, Mohammad Rezacs
dc.contributor.authorFahimi, Zahracs
dc.contributor.authorXiao, Yipingcs
dc.contributor.authorWang, Taocs
dc.contributor.authorBukvišová, Kristýnacs
dc.contributor.authorKolíbal, Miroslavcs
dc.contributor.authorRoldán, Juan Bautistacs
dc.contributor.authorPerez, Davidcs
dc.contributor.authorAguirre, Fernandocs
dc.contributor.authorLanza, Mariocs
dc.coverage.issue3cs
dc.coverage.volume4cs
dc.date.issued2022-03-21cs
dc.description.abstractMemristive electronic synapses are attractive to construct artificial neural networks (ANNs) for neuromorphic computing systems, owing to their excellent electronic performance, high integration density, and low cost. However, the necessity of initializing their conductance through a forming process requires additional peripheral hardware and complex programming algorithms. Herein, the first fabrication of memristors that are initially in low-resistive state (LRS) is reported, which exhibit homogenous initial resistance and switching voltages. When used as electronic synapses in a neuromorphic system to classify images from the CIFAR-10 dataset (Canadian Institute For Advanced Research), the memristors offer x1.83 better throughput per area and consume x0.85 less energy than standard memristors (i.e., with the necessity of forming), which stems from approximate to 63% better density and approximate to 17% faster operation. It is demonstrated in the results that tuning the local properties of materials embedded in memristive electronic synapses is an attractive strategy that can lead to an improved neuromorphic performance at the system level.en
dc.formattextcs
dc.format.extent2200001-220001cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationAdvanced Intelligent Systems. 2022, vol. 4, issue 3, p. 2200001-220001.en
dc.identifier.doi10.1002/aisy.202200001cs
dc.identifier.issn2640-4567cs
dc.identifier.orcid0000-0001-8530-8298cs
dc.identifier.orcid0000-0002-2751-5608cs
dc.identifier.other177525cs
dc.identifier.researcheridD-9301-2012cs
dc.identifier.urihttp://hdl.handle.net/11012/204285
dc.language.isoencs
dc.publisherWileycs
dc.relation.ispartofAdvanced Intelligent Systemscs
dc.relation.urihttps://onlinelibrary.wiley.com/doi/10.1002/aisy.202200001cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2640-4567/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectforming-free devicesen
dc.subjectlow-resistive stateen
dc.subjectmemristorsen
dc.subjectneuromorphic systemsen
dc.subjecttitanium dioxideen
dc.titleMemristors with Initial Low-Resistive State for Efficient Neuromorphic Systemsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-177525en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:48:19en
sync.item.modts2025.01.17 16:50:43en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav fyzikálního inženýrstvícs
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Příprava a charakterizace nanostrukturcs
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Ústav fyzikálního inženýrství
Příprava a charakterizace nanostruktur