Oxide-Based Electrolyte-Gated Transistors with Stable and Tunable Relaxation Responses for Deep Time-Delayed Reservoir Computing

Time-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 Henon map prediction tasks are implemented with high accuracy (approximate to 92.2%) and ultralow normalized root mean square error (approximate to 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.