Investigating Series and Parallel Oxide Memtransistors for Tunable Weight Update Properties-Reference-Cited by-同舟云学术

Investigating Series and Parallel Oxide Memtransistors for Tunable Weight Update Properties

Published:2023-05-27 Issue:6 Volume:5 Page:3232-3240
ISSN:2637-6113
Container-title:ACS Applied Electronic Materials
language:en
Short-container-title:ACS Appl. Electron. Mater.

Author:

Kang Seung-Hyeon¹^ORCID,Yang Seonguk²,Lee Donghyun²^ORCID,Kim Sungkyu³^ORCID,Suh Joonki²⁴^ORCID,Lee Hong-Sub⁵⁶^ORCID

Affiliation:

1. Department of Materials Science & Engineering, Kangwon National University, Chuncheon 24341, Korea

2. Department of Materials Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea

3. HMC, Department of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul 05006, Republic of Korea

4. Graduate School of Semiconductor Materials and Devices Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea

5. Department of Advanced Materials Engineering for Information and Electronics, Kyung Hee University, Yongin 17104, Republic of Korea

6. Integrated Education Institute for Frontier Science & Technology (BK21 Four), Kyung Hee University, Yongin 17104, Republic of Korea

Funder

National Research Foundation of Korea

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Electrochemistry,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acsaelm.3c00325

Reference51 articles.

1. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 2014.

2. Datapath Extension of NPUs to Support Nonconvolutional Layers Efficiently

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