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

TL;DR

This paper demonstrates a redox-based ion-gating reservoir for physical reservoir computing, showing improved prediction accuracy and memory capacity through the addition of ion gating, outperforming previous physical reservoirs.

Contribution

Introduces a novel redox-IGR device utilizing double reservoir states, enhancing performance in nonlinear dynamic tasks and memory capacity in reservoir computing.

Findings

Achieved lowest prediction error of 5.06x10^-4 with ion gating.

Performed well on NARMA2 benchmark with NMSE of 0.163.

Memory capacity increased from 1.87 to 2.73 with ion gating.

Abstract

We have demonstrated physical reservoir computing with a redox-based ion-gating reservoir (redox-IGR) comprising LixWO3 thin film and lithium ion conducting glass ceramic (LICGC). The subject redox-IGR successfully solved 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 7.39x10-4. Performance was enhanced by the addition of IG to the reservoir states, resulting in a significant lowering of the prediction error to 5.06x10-4, which is noticeably lower than other types of physical reservoirs reported to date. A second-order nonlinear autoregressive moving average (NARMA2) task, a typical benchmark of reservoir computing, was also performed with the IGR and good performance was achieved, with an NMSE of 0.163. A short-term memory task was performed to investigate an enhancement mechanism resulting from the IG addition. An increase in memory capacity, from 1.87 without IG to 2.73 with IG, was observed in the forgetting curves, indicating that enhancement of both high dimensionality and memory capacity are attributed to the origin of the performance improvement.