Redox-based ion-gating reservoir consisting of (104) oriented LiCoO 2 film, assisted by physical masking.

Reservoir computing (RC) is a machine learning framework suitable for processing time series data, and is a computationally inexpensive and fast learning model. A physical reservoir is a hardware implementation of RC using a physical system, which is expected to become the social infrastructure of a data society that needs to process vast amounts of information. Ion-gating reservoirs (IGR) are compact and suitable for integration with various physical reservoirs, but the prediction accuracy and operating speed of redox-IGRs using WO 3 as the channel are not sufficient due to irreversible Li + trapping in the WO 3 matrix during operation. Here, in order to enhance the computation performance of redox-IGRs, we developed a redox-based IGR using a (104) oriented LiCoO 2 thin film with high electronic and ionic conductivity as a trap-free channel material. The subject IGR utilizes resistance change that is due to a redox reaction (LiCoO 2 ⟺ Li 1-x CoO 2  + xLi +  + xe - ) with the insertion and desertion of Li + . The prediction error in the subject IGR was reduced by 72% and the operation speed was increased by 4 times compared to the previously reported WO 3 , which changes are due to the nonlinear and reversible electrical response of LiCoO 2 and the high dimensionality enhanced by a newly developed physical masking technique. This study has demonstrated the possibility of developing high-performance IGRs by utilizing materials with stronger nonlinearity and by increasing output dimensionality.