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

Abstract

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 (ID) is used for the reservoir states, the lowest prediction error is 8.15 × 10−4. Performance is enhanced by the addition of IG to the reservoir states, resulting in a significant lowering of the prediction error to 5.39 × 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 IG addition. An increase in memory capacity, from 2.35 without IG to 3.57 with IG, 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.