Efficient data processing using tunable entropy-stabilized oxide memristors-Reference-Cited by-同舟云学术

Efficient data processing using tunable entropy-stabilized oxide memristors

Published:2024-05-20 Issue:6 Volume:7 Page:466-474
ISSN:2520-1131
Container-title:Nature Electronics
language:en
Short-container-title:Nat Electron

Author:

Yoo Sangmin^ORCID,Chae Sieun^ORCID,Chiang Tony^ORCID,Webb Matthew^ORCID,Ma Tao^ORCID,Paik Hanjong^ORCID,Park Yongmo^ORCID,Williams Logan^ORCID,Nomoto Kazuki,Xing Huili G.^ORCID,Trolier-McKinstry Susan,Kioupakis Emmanouil,Heron John T.^ORCID,Lu Wei D.^ORCID

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41928-024-01169-1.pdf

Reference49 articles.

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2. Zidan, M. A. et al. A general memristor-based partial differential equation solver. Nat. Electron 1, 411–420 (2018).

3. Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).

4. Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).

5. Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22–29 (2018).

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