Linear conductance update improvement of CMOS-compatible second-order memristors for fast and energy-efficient training of a neural network using a memristor crossbar array-Reference-Cited by-同舟云学术

Linear conductance update improvement of CMOS-compatible second-order memristors for fast and energy-efficient training of a neural network using a memristor crossbar array

Published:2023 Issue:10 Volume:8 Page:1366-1376
ISSN:2055-6756
Container-title:Nanoscale Horizons
language:en
Short-container-title:Nanoscale Horiz.

Author:

Park See-On¹^ORCID,Park Taehoon¹,Jeong Hakcheon¹,Hong Seokman¹,Seo Seokho¹,Kwon Yunah²,Lee Jongwon²,Choi Shinhyun¹^ORCID

Affiliation:

1. The School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea

2. Nano Convergence Technology Division, National Nanofab Center (NNFC), 291, Daehak-ro, Daejeon 34141, Republic of Korea

Abstract

The linear conductance update of a CMOS-compatible HfO2 memristor is improved by introducing a second-order memristor effect and connecting a voltage divider to the device, which makes the memristor crossbar array more energy- and time-efficient.

Funder

National Research Foundation of Korea

National NanoFab Center

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2023/NH/D3NH00121K

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