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dc.contributor.authorBisquert, Juan
dc.contributor.authorBou, Agustín
dc.contributor.authorGuerrero, Antonio
dc.contributor.authorHernández-Balaguera, Enrique
dc.date.accessioned2023-11-24T07:30:45Z
dc.date.available2023-11-24T07:30:45Z
dc.date.issued2023-09
dc.identifier.citationJuan Bisquert, Agustín Bou, Antonio Guerrero, Enrique Hernández-Balaguera; Resistance transient dynamics in switchable perovskite memristors. APL Mach. Learn. 1 September 2023; 1 (3): 036101. https://doi.org/10.1063/5.0153289ca_CA
dc.identifier.issn2770-9019
dc.identifier.urihttp://hdl.handle.net/10234/204957
dc.description.abstractMemristor devices have been investigated for their properties of resistive modulation that can be used in data storage and brain-like computation elements as artificial synapses and neurons. Memristors are characterized by an onset of high current values under applied voltage that produces a transition to a low resistance state or successively to different stable states of increasing conductivity that implement synaptic weights. Here, we develop a nonlinear model to explain the variation with time of the voltage and the resistance and compare it to experimental results on ionic–electronic halide perovskite memristors. We find separate experimental signatures of the capacitive discharge and inductive current increase. We show that the capacitor produces an increase step of the resistance due to the influence of the series resistance. In contrast, the inductor feature associated with inverted hysteresis causes a decrease of the resistance, as observed experimentally. The chemical inductor feature dominates the potentiation effect in which the conductivity increases with the voltage stimulus. Our results enable a quantitative characterization of highly nonlinear electronic devices using a combination of techniques such as time transient decays and impedance spectroscopy.ca_CA
dc.format.extent10 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherAIP Publishingca_CA
dc.relation.isPartOfAPL Machine Learning, 2023, vol. 1, no 3ca_CA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/ca_CA
dc.subjectmemristorca_CA
dc.subjectelectronic devicesca_CA
dc.subjectcircuit theoremsca_CA
dc.subjectelectric measurementsca_CA
dc.subjectdata storage and retrievalca_CA
dc.subjectelectrochemical impedance spectroscopyca_CA
dc.subjectperovskitesca_CA
dc.subjectresistive switchingca_CA
dc.subjectnanomaterialsca_CA
dc.titleResistance transient dynamics in switchable perovskite memristorsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1063/5.0153289
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://pubs.aip.org/aip/aml/article/1/3/036101/2900748ca_CA
dc.description.sponsorshipThis study forms part of the Advanced Materials program and was supported by MCIN with funding from the European Union Next Generation EU (Grant No. PRTR-C17. I1) and Generalitat Valenciana. This work received funding from the Universidad Rey Juan Carlos, Project No. M2993
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA
project.funder.nameEuropean Unionca_CA
project.funder.nameUniversidad Rey Juan Carlosca_CA
project.funder.nameGeneralitat Valencianaca_CA
oaire.awardNumberPRTR-C17. I1ca_CA
oaire.awardNumberM2993ca_CA
dc.subject.ods7. Energia asequible y no contaminanteca_CA


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