Detailed Information

Cited 21 time in webofscience Cited 22 time in scopus
Metadata Downloads

Diverse synaptic weight adjustment of bio-inspired ZrOx-based memristors for neuromorphic system

Full metadata record
DC Field Value Language
dc.contributor.authorKim, Chaeun-
dc.contributor.authorLee, Yunseok-
dc.contributor.authorKim, Sunghun-
dc.contributor.authorKang, Myounggon-
dc.contributor.authorKim, Sungjun-
dc.date.accessioned2024-08-08T10:01:49Z-
dc.date.available2024-08-08T10:01:49Z-
dc.date.issued2023-04-
dc.identifier.issn1369-8001-
dc.identifier.issn1873-4081-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/21322-
dc.description.abstractIn this article, we demonstrate the bio-inspired synaptic features of the TiN/ZrOx/Pt capacitor structure for neuromorphic engineering. The chemical and material compositions and the thicknesses of each of the layers are verified by using transmission electron microscopy (TEM) images and energy-dispersive X-ray spectroscopy (EDS) maps. Stable resistive switching with a low set voltage (-1 V) was determined by scanning the DC I-V curves of many cells. The DC endurance of-104 cycles and retention (10,000 s) in five states was achieved. Multi-level cells (MLC) characteristics were achieved based on the compliance current and reset stop voltage in DC sweep and pulses. Finally, we emulated paired-pulse facilitation (PPF), paired-pulse depression (PPD), electric excitatory postsynaptic current (EPSC), and spike-timing-dependent plasticity (STDP) of the artificial synapse by using the RRAM device.-
dc.format.extent6-
dc.language영어-
dc.language.isoENG-
dc.publisherElsevier Ltd-
dc.titleDiverse synaptic weight adjustment of bio-inspired ZrOx-based memristors for neuromorphic system-
dc.typeArticle-
dc.publisher.location네델란드-
dc.identifier.doi10.1016/j.mssp.2023.107314-
dc.identifier.scopusid2-s2.0-85146051055-
dc.identifier.wosid000925289600001-
dc.identifier.bibliographicCitationMaterials Science in Semiconductor Processing, v.157, pp 1 - 6-
dc.citation.titleMaterials Science in Semiconductor Processing-
dc.citation.volume157-
dc.citation.startPage1-
dc.citation.endPage6-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.relation.journalWebOfScienceCategoryPhysics, Condensed Matter-
dc.subject.keywordPlusRESISTIVE SWITCHING PROPERTIES-
dc.subject.keywordPlusDEVICES-
dc.subject.keywordPlusMEMORY-
dc.subject.keywordPlusPOWER-
dc.subject.keywordPlusFILM-
dc.subject.keywordAuthorAI semiconductor-
dc.subject.keywordAuthorNeural network-
dc.subject.keywordAuthorNeuromorphic system-
dc.subject.keywordAuthorMemristor-
dc.subject.keywordAuthorResistive switching-
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