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- Publisher Website: 10.1002/aelm.201800876
- Scopus: eid_2-s2.0-85065329402
- WOS: WOS:000486206400010
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Article: Low-Conductance and Multilevel CMOS-Integrated Nanoscale Oxide Memristors
Title | Low-Conductance and Multilevel CMOS-Integrated Nanoscale Oxide Memristors |
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Authors | |
Keywords | tantalum oxide hardware accelerators memristor neuromorphic computing |
Issue Date | 2019 |
Citation | Advanced Electronic Materials, 2019, v. 5, n. 9, article no. 1800876 How to Cite? |
Abstract | © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Using memristors, such as oxide and phase change resistive switches, as tunable resistors to construct analog computing hardware accelerators is gaining keen attention. Such accelerators have demonstrated the potential to significantly outperform digital computers in highly relevant applications such as machine learning and image processing. However, improvements in device-level performance of memristors, including reducing power consumption and high current–induced metal migration in interconnects, need continued developments. Nanoscaling and complementary metal-oxide semiconductor (CMOS) integration are also of significant importance in commercialization of such accelerators. Here tantalum oxide memristors scaled down to 25 nm sizes and integrated on CMOS transistor circuits are presented. The memristor conductance is programmable with a 6 order-of-magnitude operating range, especially with 3-bits below 10 µS for low current operation. The stability of such levels and the size scaling of the operating parameters are further studied. These results will aid device engineering of memristors and bolster development of neuromorphic hardware accelerators. |
Description | Accepted manuscript is available on the publisher website. |
Persistent Identifier | http://hdl.handle.net/10722/286989 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Sheng, Xia | - |
dc.contributor.author | Graves, Catherine E. | - |
dc.contributor.author | Kumar, Suhas | - |
dc.contributor.author | Li, Xuema | - |
dc.contributor.author | Buchanan, Brent | - |
dc.contributor.author | Zheng, Le | - |
dc.contributor.author | Lam, Sity | - |
dc.contributor.author | Li, Can | - |
dc.contributor.author | Strachan, John Paul | - |
dc.date.accessioned | 2020-09-07T11:46:12Z | - |
dc.date.available | 2020-09-07T11:46:12Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Advanced Electronic Materials, 2019, v. 5, n. 9, article no. 1800876 | - |
dc.identifier.uri | http://hdl.handle.net/10722/286989 | - |
dc.description | Accepted manuscript is available on the publisher website. | - |
dc.description.abstract | © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Using memristors, such as oxide and phase change resistive switches, as tunable resistors to construct analog computing hardware accelerators is gaining keen attention. Such accelerators have demonstrated the potential to significantly outperform digital computers in highly relevant applications such as machine learning and image processing. However, improvements in device-level performance of memristors, including reducing power consumption and high current–induced metal migration in interconnects, need continued developments. Nanoscaling and complementary metal-oxide semiconductor (CMOS) integration are also of significant importance in commercialization of such accelerators. Here tantalum oxide memristors scaled down to 25 nm sizes and integrated on CMOS transistor circuits are presented. The memristor conductance is programmable with a 6 order-of-magnitude operating range, especially with 3-bits below 10 µS for low current operation. The stability of such levels and the size scaling of the operating parameters are further studied. These results will aid device engineering of memristors and bolster development of neuromorphic hardware accelerators. | - |
dc.language | eng | - |
dc.relation.ispartof | Advanced Electronic Materials | - |
dc.subject | tantalum oxide | - |
dc.subject | hardware accelerators | - |
dc.subject | memristor | - |
dc.subject | neuromorphic computing | - |
dc.title | Low-Conductance and Multilevel CMOS-Integrated Nanoscale Oxide Memristors | - |
dc.type | Article | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1002/aelm.201800876 | - |
dc.identifier.scopus | eid_2-s2.0-85065329402 | - |
dc.identifier.volume | 5 | - |
dc.identifier.issue | 9 | - |
dc.identifier.spage | article no. 1800876 | - |
dc.identifier.epage | article no. 1800876 | - |
dc.identifier.eissn | 2199-160X | - |
dc.identifier.isi | WOS:000486206400010 | - |
dc.identifier.issnl | 2199-160X | - |