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Article: Review of memristor devices in neuromorphic computing: Materials sciences and device challenges

TitleReview of memristor devices in neuromorphic computing: Materials sciences and device challenges
Authors
Keywordsmemristive devices
Memristor
neuromorphic computing
Issue Date2018
Citation
Journal of Physics D: Applied Physics, 2018, v. 51, n. 50, article no. 503002 How to Cite?
Abstract© 2018 IOP Publishing Ltd. The memristor is considered as the one of the promising candidates for next generation computing systems. Novel computing architectures based on memristors have shown great potential in replacing or complementing conventional computing platforms based on the von Neumann architecture which faces challenges in the big-data era such as the memory wall. However, there are a number of technical challenges in implementing memristor based computing. In this review, we focus on the research performed on the memristor material stacks and their compatibility with CMOS processes, the electrical performance, and the integration. In addition, recent demonstrations of neuromorphic computing using memristors are surveyed.
Persistent Identifierhttp://hdl.handle.net/10722/286975
ISSN
2021 Impact Factor: 3.409
2020 SCImago Journal Rankings: 0.857
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Yibo-
dc.contributor.authorWang, Zhongrui-
dc.contributor.authorMidya, Rivu-
dc.contributor.authorXia, Qiangfei-
dc.contributor.authorJoshua Yang, J.-
dc.date.accessioned2020-09-07T11:46:10Z-
dc.date.available2020-09-07T11:46:10Z-
dc.date.issued2018-
dc.identifier.citationJournal of Physics D: Applied Physics, 2018, v. 51, n. 50, article no. 503002-
dc.identifier.issn0022-3727-
dc.identifier.urihttp://hdl.handle.net/10722/286975-
dc.description.abstract© 2018 IOP Publishing Ltd. The memristor is considered as the one of the promising candidates for next generation computing systems. Novel computing architectures based on memristors have shown great potential in replacing or complementing conventional computing platforms based on the von Neumann architecture which faces challenges in the big-data era such as the memory wall. However, there are a number of technical challenges in implementing memristor based computing. In this review, we focus on the research performed on the memristor material stacks and their compatibility with CMOS processes, the electrical performance, and the integration. In addition, recent demonstrations of neuromorphic computing using memristors are surveyed.-
dc.languageeng-
dc.relation.ispartofJournal of Physics D: Applied Physics-
dc.subjectmemristive devices-
dc.subjectMemristor-
dc.subjectneuromorphic computing-
dc.titleReview of memristor devices in neuromorphic computing: Materials sciences and device challenges-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1088/1361-6463/aade3f-
dc.identifier.scopuseid_2-s2.0-85055675317-
dc.identifier.volume51-
dc.identifier.issue50-
dc.identifier.spagearticle no. 503002-
dc.identifier.epagearticle no. 503002-
dc.identifier.eissn1361-6463-
dc.identifier.isiWOS:000445501000001-
dc.identifier.issnl0022-3727-

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