In-materio reservoir working at low frequencies in a Ag<sub>2</sub>S-island network-Reference-Cited by-同舟云学术

In-materio reservoir working at low frequencies in a Ag₂S-island network

Published:2022 Issue:20 Volume:14 Page:7634-7640
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Nakajima Motoharu¹,Minegishi Kazuki¹,Shimizu Yosuke¹,Usami Yuki²³,Tanaka Hirofumi²³^ORCID,Hasegawa Tsuyoshi¹^ORCID

Affiliation:

1. Department of Pure and Applied Physics, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan

2. Department of Human Intelligence Systems, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu 808-0196, Japan

3. Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, Japan

Abstract

A reservoir that is more sensitive to lower frequencies is developed by a Ag2S-island network, where Ag filament growth/shrinkage achieves non-linear transformation of input signals. Six logic operations are achieved with accuracy higher than 99%.

Funder

Japan Science and Technology Corporation

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2022/NR/D2NR01439D

Reference56 articles.

1. Biological plausibility and stochasticity in scalable VO2 active memristor neurons

2. Deep learning

3. Power-constrained CMOS scaling limits