A Recoverable Synapse Device Using a Three-Dimensional Silicon Transistor

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dc.contributor.authorHur, Jaeko
dc.contributor.authorJang, Byung Chulko
dc.contributor.authorPark, Jihunko
dc.contributor.authorMoon, Dong-Ilko
dc.contributor.authorBae, Hagyoulko
dc.contributor.authorPark, Jun-Youngko
dc.contributor.authorKim, Gun-Heeko
dc.contributor.authorJeon, Seung-Baeko
dc.contributor.authorSeo, Myungsooko
dc.contributor.authorKim, Sunghoko
dc.contributor.authorChoi, Sung-Yoolko
dc.contributor.authorChoi, Yang-Kyuko
dc.date.accessioned2018-12-20T08:04:14Z-
dc.date.available2018-12-20T08:04:14Z-
dc.date.created2018-12-03-
dc.date.created2018-12-03-
dc.date.created2018-12-03-
dc.date.issued2018-11-
dc.identifier.citationADVANCED FUNCTIONAL MATERIALS, v.28, no.47-
dc.identifier.issn1616-301X-
dc.identifier.urihttp://hdl.handle.net/10203/248736-
dc.description.abstractTo prepare for the upcoming big-data era, numerous attempts are underway to develop a neuromorphic system which is capable of imitating a biologic neural network. Despite the significant improvements to software-based artificial neural networks (ANNs) recently, they remain inefficient in terms of energy use. Alternatively, many researchers have been attracted to hardware-based ANNs by fundamentally mimicking neural circuits and synapses. In this study, a two-terminal silicon-channel synapse (SINAPSE) with a poly-Si/SiO2/Si3N4 gate stack over a silicon channel is introduced, and demonstrated the smallest size of a synapse device reported thus far, along with reliable, low-power performance. A distinctive feature of SINAPSE is that it emulates synaptic recovery, a retrieval process for neurotransmitters which would be otherwise depleted. By applying an electrical recovery pulse to SINAPSE, synaptic recovery was for the first time successfully imitated. Experimental results demonstrate the potential of the curable SINAPSE as a fundamental unit in neuromorphic circuitry.-
dc.languageEnglish-
dc.publisherWILEY-V C H VERLAG GMBH-
dc.titleA Recoverable Synapse Device Using a Three-Dimensional Silicon Transistor-
dc.typeArticle-
dc.identifier.wosid000450371400007-
dc.identifier.scopusid2-s2.0-85054175855-
dc.type.rimsART-
dc.citation.volume28-
dc.citation.issue47-
dc.citation.publicationnameADVANCED FUNCTIONAL MATERIALS-
dc.identifier.doi10.1002/adfm.201804844-
dc.contributor.localauthorChoi, Sung-Yool-
dc.contributor.localauthorChoi, Yang-Kyu-
dc.contributor.nonIdAuthorPark, Jihun-
dc.contributor.nonIdAuthorMoon, Dong-Il-
dc.contributor.nonIdAuthorKim, Gun-Hee-
dc.contributor.nonIdAuthorKim, Sungho-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorartificial neural networks (ANN)-
dc.subject.keywordAuthorfin field-effect transistors (FinFET)-
dc.subject.keywordAuthorneuromorphic systems-
dc.subject.keywordAuthorsilicon synapses (SINAPSE)-
dc.subject.keywordAuthorsynapse devices-
dc.subject.keywordAuthorsynaptic fatigue-
dc.subject.keywordAuthorsynaptic recovery-
dc.subject.keywordPlusNONVOLATILE MEMORY DEVICES-
dc.subject.keywordPlusLONG-TERM POTENTIATION-
dc.subject.keywordPlusPATTERN-RECOGNITION-
dc.subject.keywordPlusSYSTEMS-
dc.subject.keywordPlusPLASTICITY-
dc.subject.keywordPlusNEUROTRANSMITTERS-
dc.subject.keywordPlusHIPPOCAMPUS-
dc.subject.keywordPlusCOMPUTATION-
dc.subject.keywordPlusRETENTION-
dc.subject.keywordPlusFATIGUE-
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