Affiliation:
1. Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics of the Chinese Academy of Sciences Beijing 100029 China
2. University of Chinese Academy of Sciences Beijing 101408 China
3. Department of Electrical and Electronic Engineering The University of Hong Kong Pok Fu Lam Road Hong Kong 999077 China
Abstract
AbstractTime‐delayed reservoir computing with marked strengths of friendly hardware implementation and low training cost is regarded as a promising solution to realize time and energy‐efficient time series information processing and thus receives growing attention. However, achieving a sufficient number of reproducible reservoir states remains a significant challenge, which severely limits its computing performance. Here, an electric‐double‐layer‐coupled oxide‐based electrolyte‐gated transistor with a shared gate and varying channel lengths is developed to construct a deep time‐delayed reservoir computing system. A variety of short‐term synaptic responses related to inherent ion‐electron‐coupled dynamics at the electrolyte/channel interface are demonstrated, reflecting a flexibly regulable channel current. Different stable and tunable relaxation responses corresponding to varying channel lengths are obtained to enrich reservoir states combined with virtual nodes ways. The spoken‐digit classification and Hénon map prediction tasks are implemented with high accuracy (≈92.2%) and ultralow normalized root mean square error (≈0.013), respectively, validating the significant improvement of the computing performance by introducing additional relaxation responses. This work opens a promising pathway in exploiting oxide‐based electrolyte‐gated transistors for realizing temporal information processing hardware systems.
Funder
National Basic Research Program of China
National Natural Science Foundation of China
Subject
Electronic, Optical and Magnetic Materials