Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Highly Stable Artificial Synapse Consisting of Low-Surface Defect van der Waals and Self-Assembled Materials

Full metadata record
DC Field Value Language
dc.contributor.authorOh Seyong-
dc.contributor.authorJung Sooyoung-
dc.contributor.authorAli Muhammad Hasnain-
dc.contributor.authorKim Jeong-Hoon-
dc.contributor.authorKim Hyeongjun-
dc.contributor.authorPark Jin-Hong-
dc.date.accessioned2023-08-16T07:30:02Z-
dc.date.available2023-08-16T07:30:02Z-
dc.date.issued2020-08-
dc.identifier.issn1944-8244-
dc.identifier.issn1944-8252-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/113739-
dc.description.abstractThe long-term plasticity of biological synapses was successfully emulated in an artificial synapse fabricated by combining low-surface defect van der Waals (vdW) and self-assembled (SA) materials. The synaptic operation could be achieved by facilitating hole trapping and releasing only via the amine (NH2) functional groups in 3-aminopropyltriethoxysilane, which consequently induced a gradual conductance change in the WSe2 channel. The vdW-SA synaptic device exhibited extremely stable long-term potentiation/depression (LTP/LTD) characteristics; its dynamic range and nonlinearity reproduced near 100 and 3.13/-6.53 (for LTP/LTD) with relative standard deviations (RSDs) below 2%. Furthermore, after conducting training and recognition tasks for the Modified National Institute of Standard and Technology (MNIST) digit patterns, we verified that the maximum recognition rate was 78.3%, and especially, its RSD was as low as 0.32% over several training/recognition cycles. This study provides a background for future research on advanced artificial synapses based on vdW and organic materials.-
dc.format.extent7-
dc.language영어-
dc.language.isoENG-
dc.publisherAmerican Chemical Society-
dc.titleHighly Stable Artificial Synapse Consisting of Low-Surface Defect van der Waals and Self-Assembled Materials-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1021/acsami.0c07394-
dc.identifier.scopusid2-s2.0-85089961679-
dc.identifier.wosid000566662000050-
dc.identifier.bibliographicCitationACS Applied Materials & Interfaces, v.12, no.34, pp 38299 - 38305-
dc.citation.titleACS Applied Materials & Interfaces-
dc.citation.volume12-
dc.citation.number34-
dc.citation.startPage38299-
dc.citation.endPage38305-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalWebOfScienceCategoryNanoscience & Nanotechnology-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.subject.keywordPlusMONOLAYERS-
dc.subject.keywordPlusTRANSISTORS-
dc.subject.keywordPlusLAYER-
dc.subject.keywordAuthorAPTES-
dc.subject.keywordAuthorartificial synapses-
dc.subject.keywordAuthorneuromorphic computing-
dc.subject.keywordAuthorpattern recognition-
dc.subject.keywordAuthorvdW materials-
dc.identifier.urlhttps://pubs.acs.org/doi/10.1021/acsami.0c07394?src=getftr-
Files in This Item
Go to Link
Appears in
Collections
COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher OH, SEYONG photo

OH, SEYONG
ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
Read more

Altmetrics

Total Views & Downloads

BROWSE