A Redox-Based Ion-Gating Reservoir, Utilizing Double Reservoir States in Drain and Gate Nonlinear Responses

Article; Early Access

关键词: IN-SITU;    ATOMIC SWITCH;    MEMRISTOR;    CLASSIFICATION;    DEGRADATION;    DYNAMICS;    NETWORK;    FILM;   

DOI  :  10.1002/aisy.202300123

来源: SCIE

【 摘 要 】

Herein, physical reservoir computing with a redox-based ion-gating reservoir (redox-IGR) comprising LixWO3 thin film and lithium-ion conducting glass ceramic (LICGC) is demonstrated. The subject redox-IGR successfully solves a second-order nonlinear dynamic equation by utilizing voltage pulse driven ion-gating in a LixWO3 channel to enable reservoir computing. Under the normal conditions, in which only the drain current (I-D) is used for the reservoir states, the lowest prediction error is 8.15 x 10(-4). Performance is enhanced by the addition of I-G to the reservoir states, resulting in a significant lowering of the prediction error to 5.39 x 10(-4), which is noticeably lower than other types of physical reservoirs (memristors and spin torque oscillators) reported to date. A second-order nonlinear autoregressive moving average (NARMA2) task, a typical benchmark of reservoir computing, is also performed with the IGR and good performance is achieved, with a normalized mean square error (NMSE) of 0.163. A short-term memory task is performed to investigate an enhancement mechanism resulting from the I-G addition. An increase in memory capacity, from 2.35 without I-G to 3.57 with I-G, is observed in the forgetting curves, indicating that enhancement of both high dimensionality and memory capacity is attributed to the origin of the performance improvement.