A Learning‐Rate Modulable and Reliable TiO <i> <sub>x</sub> </i> Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing-Reference-Cited by-同舟云学术

A Learning‐Rate Modulable and Reliable TiO _x Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing

Published:2022-06-05 Issue:22 Volume:9 Page:2201117
ISSN:2198-3844
Container-title:Advanced Science
language:en
Short-container-title:Advanced Science

Author:

Jang Jingon¹,Gi Sanggyun²,Yeo Injune²,Choi Sanghyeon¹,Jang Seonghoon¹,Ham Seonggil¹,Lee Byunggeun²,Wang Gunuk¹³⁴^ORCID

Affiliation:

1. KU‐KIST Graduate School of Converging Science and Technology Korea University 145, Anam‐ro, Seongbuk‐gu Seoul 02841 Republic of Korea

2. School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology 123, Cheomdangwagi‐ro, Buk‐gu, Gwangju, Republic of Korea Buk‐gu 61005 Republic of Korea

3. Department of Integrative Energy Engineering Korea University 145, Anam‐ro, Seongbuk‐gu Seoul 02841 Republic of Korea

4. Center for Neuromorphic Engineering Korea Institute of Science and Technology 5, Hwarang‐ro 14‐gil, Seongbuk‐gu Seoul 02792 Republic of Korea

Funder

National Research Foundation of Korea

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202201117

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2. Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol

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5. Review of memristor devices in neuromorphic computing: materials sciences and device challenges

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