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정홍식

Jeong, Hongsik
Future Semiconductor Technology Lab.
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dc.citation.endPage 158 -
dc.citation.startPage 153 -
dc.citation.title NEUROCOMPUTING -
dc.citation.volume 155 -
dc.contributor.author Kang, Dae-Hwan -
dc.contributor.author Jun, Hyun-Goo -
dc.contributor.author Ryoo, Kyung-Chang -
dc.contributor.author Jeong, Hongsik -
dc.contributor.author Sohn, Hyunchul -
dc.date.accessioned 2023-12-22T01:12:35Z -
dc.date.available 2023-12-22T01:12:35Z -
dc.date.created 2019-07-11 -
dc.date.issued 2015-05 -
dc.description.abstract The spike-timing dependent plasticity (STDP) of biological synapses, which is known to be a function of the formulated Hebbian learning rule of human cognition, learning and memory abilities, was emulated with two-phase change memory (2-PCM) cells built with 39 nm technology. For this, we designed a novel time-modulated voltage (TMV) scheme for changing the conductance of 2-PCM cells, that could produce both long-term potentiation (LTP) and long-term depression (LTD) by applying variable (decreasing/increasing) pulse voltages according to the sign and magnitude in time interval between pre- and post-spikes. Since such schemes can be easily modified to have a variety of pulse shapes and time intervals between pulses, it is expected to be a proper scheme for designing diverse synaptic connection abilities. In addition, the small form factor and low energy consumption of 2-PCM make them comparable to biological synapses, which makes phase change memory a promising candidate for electronic synapses in large-scale neuromorphic system applications. -
dc.identifier.bibliographicCitation NEUROCOMPUTING, v.155, pp.153 - 158 -
dc.identifier.doi 10.1016/j.neucom.2014.12.036 -
dc.identifier.issn 0925-2312 -
dc.identifier.scopusid 2-s2.0-84922824950 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/27134 -
dc.identifier.url https://www.sciencedirect.com/science/article/pii/S0925231214017007?via%3Dihub -
dc.identifier.wosid 000350934600017 -
dc.language 영어 -
dc.publisher ELSEVIER SCIENCE BV -
dc.title Emulation of spike-timing dependent plasticity in nano-scale phase change memory -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Computer Science, Artificial Intelligence -
dc.relation.journalResearchArea Computer Science -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Spike-timing dependent plasticity -
dc.subject.keywordAuthor Two-phase change memory cells -
dc.subject.keywordAuthor Long-term potentiation -
dc.subject.keywordAuthor Long-term depression -
dc.subject.keywordAuthor Electronic synapses -

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