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- Publisher Website: 10.1088/1361-6463/aade3f
- Scopus: eid_2-s2.0-85055675317
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Article: Review of memristor devices in neuromorphic computing: Materials sciences and device challenges
Title | Review of memristor devices in neuromorphic computing: Materials sciences and device challenges |
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Authors | |
Keywords | memristive devices Memristor neuromorphic computing |
Issue Date | 2018 |
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 Identifier | http://hdl.handle.net/10722/286975 |
ISSN | 2021 Impact Factor: 3.409 2020 SCImago Journal Rankings: 0.857 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Li, Yibo | - |
dc.contributor.author | Wang, Zhongrui | - |
dc.contributor.author | Midya, Rivu | - |
dc.contributor.author | Xia, Qiangfei | - |
dc.contributor.author | Joshua Yang, J. | - |
dc.date.accessioned | 2020-09-07T11:46:10Z | - |
dc.date.available | 2020-09-07T11:46:10Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Journal of Physics D: Applied Physics, 2018, v. 51, n. 50, article no. 503002 | - |
dc.identifier.issn | 0022-3727 | - |
dc.identifier.uri | http://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.language | eng | - |
dc.relation.ispartof | Journal of Physics D: Applied Physics | - |
dc.subject | memristive devices | - |
dc.subject | Memristor | - |
dc.subject | neuromorphic computing | - |
dc.title | Review of memristor devices in neuromorphic computing: Materials sciences and device challenges | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1088/1361-6463/aade3f | - |
dc.identifier.scopus | eid_2-s2.0-85055675317 | - |
dc.identifier.volume | 51 | - |
dc.identifier.issue | 50 | - |
dc.identifier.spage | article no. 503002 | - |
dc.identifier.epage | article no. 503002 | - |
dc.identifier.eissn | 1361-6463 | - |
dc.identifier.isi | WOS:000445501000001 | - |
dc.identifier.issnl | 0022-3727 | - |