Parylene-based memristive crossbar structures with multilevel resistive switching for neuromorphic computing
Currently, there is growing interest in wearable and biocompatible smart computing and information processing systems that are safe for the human body. Memristive devices are promising for solving such problems due to a number of their attractive properties, such as low power consumption, scalabilit...
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| Veröffentlicht in: | Nanotechnology 2022-06, Vol.33 (25), p.255201 |
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| creator | Shvetsov, Boris S Minnekhanov, Anton A Emelyanov, Andrey V Ilyasov, Aleksandr I Grishchenko, Yulia V Zanaveskin, Maxim L Nesmelov, Aleksandr A Streltsov, Dmitry R Patsaev, Timofey D Vasiliev, Alexander L Rylkov, Vladimir V Demin, Vyacheslav A |
| description | Currently, there is growing interest in wearable and biocompatible smart computing and information processing systems that are safe for the human body. Memristive devices are promising for solving such problems due to a number of their attractive properties, such as low power consumption, scalability, and the multilevel nature of resistive switching (plasticity). The multilevel plasticity allows memristors to emulate synapses in hardware neuromorphic computing systems (NCSs). The aim of this work was to study Cu/poly-
-xylylene(PPX)/Au memristive elements fabricated in the crossbar geometry. In developing the technology for manufacturing such samples, we took into account their characteristics, in particular stable and multilevel resistive switching (at least 10 different states) and low operating voltage ( |
| doi_str_mv | 10.1088/1361-6528/ac5cfe |
| format | Article |
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-xylylene(PPX)/Au memristive elements fabricated in the crossbar geometry. In developing the technology for manufacturing such samples, we took into account their characteristics, in particular stable and multilevel resistive switching (at least 10 different states) and low operating voltage (<2 V), suitable for NCSs. Experiments on cycle to cycle (C2C) switching of a single memristor and device to device (D2D) switching of several memristors have shown high reproducibility of resistive switching (RS) voltages. Based on the obtained memristors, a formal hardware neuromorphic network was created that can be trained to classify simple patterns.</description><identifier>ISSN: 0957-4484</identifier><identifier>EISSN: 1361-6528</identifier><identifier>DOI: 10.1088/1361-6528/ac5cfe</identifier><identifier>PMID: 35276689</identifier><identifier>CODEN: NNOTER</identifier><language>eng</language><publisher>England: IOP Publishing</publisher><subject>crossbar structure ; memristor ; neuromorphic network ; parylene ; resistive switching</subject><ispartof>Nanotechnology, 2022-06, Vol.33 (25), p.255201</ispartof><rights>2022 IOP Publishing Ltd</rights><rights>2022 IOP Publishing Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c370t-99b720a92c90f0669eab71e60beedd07cb1d270813d88cc28ab4b3a4e4c9eb093</citedby><cites>FETCH-LOGICAL-c370t-99b720a92c90f0669eab71e60beedd07cb1d270813d88cc28ab4b3a4e4c9eb093</cites><orcidid>0000-0002-7685-8463 ; 0000-0001-5393-0285 ; 0000-0001-7884-4180 ; 0000-0002-5660-778X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1361-6528/ac5cfe/pdf$$EPDF$$P50$$Giop$$H</linktopdf><link.rule.ids>314,777,781,27905,27906,53827,53874</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35276689$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shvetsov, Boris S</creatorcontrib><creatorcontrib>Minnekhanov, Anton A</creatorcontrib><creatorcontrib>Emelyanov, Andrey V</creatorcontrib><creatorcontrib>Ilyasov, Aleksandr I</creatorcontrib><creatorcontrib>Grishchenko, Yulia V</creatorcontrib><creatorcontrib>Zanaveskin, Maxim L</creatorcontrib><creatorcontrib>Nesmelov, Aleksandr A</creatorcontrib><creatorcontrib>Streltsov, Dmitry R</creatorcontrib><creatorcontrib>Patsaev, Timofey D</creatorcontrib><creatorcontrib>Vasiliev, Alexander L</creatorcontrib><creatorcontrib>Rylkov, Vladimir V</creatorcontrib><creatorcontrib>Demin, Vyacheslav A</creatorcontrib><title>Parylene-based memristive crossbar structures with multilevel resistive switching for neuromorphic computing</title><title>Nanotechnology</title><addtitle>NANO</addtitle><addtitle>Nanotechnology</addtitle><description>Currently, there is growing interest in wearable and biocompatible smart computing and information processing systems that are safe for the human body. Memristive devices are promising for solving such problems due to a number of their attractive properties, such as low power consumption, scalability, and the multilevel nature of resistive switching (plasticity). The multilevel plasticity allows memristors to emulate synapses in hardware neuromorphic computing systems (NCSs). The aim of this work was to study Cu/poly-
-xylylene(PPX)/Au memristive elements fabricated in the crossbar geometry. In developing the technology for manufacturing such samples, we took into account their characteristics, in particular stable and multilevel resistive switching (at least 10 different states) and low operating voltage (<2 V), suitable for NCSs. Experiments on cycle to cycle (C2C) switching of a single memristor and device to device (D2D) switching of several memristors have shown high reproducibility of resistive switching (RS) voltages. 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-xylylene(PPX)/Au memristive elements fabricated in the crossbar geometry. In developing the technology for manufacturing such samples, we took into account their characteristics, in particular stable and multilevel resistive switching (at least 10 different states) and low operating voltage (<2 V), suitable for NCSs. Experiments on cycle to cycle (C2C) switching of a single memristor and device to device (D2D) switching of several memristors have shown high reproducibility of resistive switching (RS) voltages. Based on the obtained memristors, a formal hardware neuromorphic network was created that can be trained to classify simple patterns.</abstract><cop>England</cop><pub>IOP Publishing</pub><pmid>35276689</pmid><doi>10.1088/1361-6528/ac5cfe</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-7685-8463</orcidid><orcidid>https://orcid.org/0000-0001-5393-0285</orcidid><orcidid>https://orcid.org/0000-0001-7884-4180</orcidid><orcidid>https://orcid.org/0000-0002-5660-778X</orcidid></addata></record> |
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| subjects | crossbar structure memristor neuromorphic network parylene resistive switching |
| title | Parylene-based memristive crossbar structures with multilevel resistive switching for neuromorphic computing |
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