Detailed Information

Cited 44 time in webofscience Cited 46 time in scopus
Metadata Downloads

Implementation of reservoir computing using volatile WOx-based memristor

Full metadata record
DC Field Value Language
dc.contributor.authorKim, Dahye-
dc.contributor.authorShin, Jiwoong-
dc.contributor.authorKim, Sungjun-
dc.date.accessioned2023-04-27T08:41:02Z-
dc.date.available2023-04-27T08:41:02Z-
dc.date.issued2022-10-
dc.identifier.issn0169-4332-
dc.identifier.issn1873-5584-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/2330-
dc.description.abstractIn this study, we investigate a Ni/WOx/ITO-glass memristor device to verify short-term memory characteristics for reservoir computing systems. We verify the chemical and material compositions of each layer using transmission electron microscopy (TEM) image and X-ray photoelectron spectroscopy (XPS). The device has a characteristic that the current decreases with time, but shows a reverse current decay phenomenon. In addition, potentiation and depression data are obtained through modulated pulses and measurement methods. Based on this result, meaningful pattern recognition accuracy is obtained. Also, it is proved that the gradual conductance modulation can be controlled through pulse amplitude and time interval between the pulses. Finally, reservoir computing is realized based on short-term characteristics of the device. All 16 states of 4 bits have been implemented, and it is proved that the changed state can be classified using a simple learning algorithm after reading it with pulses. We also propose to make the system to consume low power.-
dc.format.extent8-
dc.language영어-
dc.language.isoENG-
dc.publisherELSEVIER-
dc.titleImplementation of reservoir computing using volatile WOx-based memristor-
dc.typeArticle-
dc.publisher.location네델란드-
dc.identifier.doi10.1016/j.apsusc.2022.153876-
dc.identifier.scopusid2-s2.0-85132214054-
dc.identifier.wosid000817886500002-
dc.identifier.bibliographicCitationApplied Surface Science, v.599, pp 1 - 8-
dc.citation.titleApplied Surface Science-
dc.citation.volume599-
dc.citation.startPage1-
dc.citation.endPage8-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryChemistry, Physical-
dc.relation.journalWebOfScienceCategoryMaterials Science, Coatings & Films-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.relation.journalWebOfScienceCategoryPhysics, Condensed Matter-
dc.subject.keywordPlusEFFICIENT-
dc.subject.keywordPlusSYNAPSE-
dc.subject.keywordAuthorSynaptic device-
dc.subject.keywordAuthorShort-term memory-
dc.subject.keywordAuthorReservoir computing-
dc.subject.keywordAuthorNeuromorphic computing-
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > Department of Electronics and Electrical Engineering > 1. Journal Articles

qrcode

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

Related Researcher

Researcher Kim, Sung Jun photo

Kim, Sung Jun
College of Engineering (Department of Electronics and Electrical Engineering)
Read more

Altmetrics

Total Views & Downloads

BROWSE