Physical reservoir computing has recently been attracting attention for its ability to substantially reduce the computational resources required to process time series data. However, the physical reservoirs that have been reported to date have had insufficient computational capacity, and most of them have a large volume, which makes their practical application difficult. Here, we describe the development of a Li ⁺ electrolyte–based ion-gating reservoir (IGR), with ion-electron–coupled dynamics, for use in high-performance physical reservoir computing. A variety of synaptic responses were obtained in response to past experience, which were stored as transient charge density patterns in an electric double layer, at the Li ⁺ electrolyte/diamond interface. Performance for a second-order nonlinear dynamical equation task is one order of magnitude higher than memristor-based reservoirs. The edge-of-chaos state of the IGR enabled the best computational capacity. The IGR described here opens the way for high-performance and integrated neural network devices.

The electric double layer (EDL) effect at solid electrolyte/electrode interfaces has been a key topic in many energy and nanoelectronics applications (e.g., all-solid-state Li⁺ batteries and memristors). However, its characterization remains difficult in comparison with liquid electrolytes. Herein, we use a novel method to show that the EDL effect, and its suppression at solid electrolyte/electronic material interfaces, can be characterized on the basis of the electric conduction characteristics of hydrogenated diamond(H-diamond)-based EDL transistors (EDLTs). Whereas H-diamond-based EDLT with a Li-Si-Zr-O Li⁺ solid electrolyte showed EDL-induced hole density modulation over a range of up to three orders of magnitude, EDLT with a Li-La-Ti-O (LLTO) Li⁺ solid electrolyte showed negligible enhancement, which indicates strong suppression of the EDL effect. Such suppression is attributed to charge neutralization in the LLTO, which is due to variation in the valence state of the Ti ions present. The method described is useful for quantitatively evaluating the EDL effect in various solid electrolytes.

An all-solid-state neuromorphic transistor composed of a WO ₃ thin film and a proton-conducting electrolyte was fabricated for application to next-generation information and communication technology including artificial neural networks. The drain current exhibited a 4-order-of-magnitude increment by redox reaction of the WO ₃ thin film owing to proton migration. Learning and forgetting characteristics were well tuned by the gate control of WO ₃ redox reactions owing to the separation of the current reading path and pulse application path in the transistor structure. This technique should lead to the development of versatile and low-power-consumption neuromorphic devices.

Yttria-stabilized zirconia (Zr_0.92Y_0.08O_2-δ :YSZ) thin films have prepared on Al₂O₃ substrates by RF magnetron sputtering. The as-deposited YSZ thin film prepared at 500 ºC exhibits the (020) peak at 2θ ~34.8^°, which corresponds to the peak position of the YSZ single crystal. The Zr⁴⁺, Y³⁺ and valence states were confirmed by photoemission spectroscopy (PES). The YSZ thin film at wet air exhibits higher conductivity and lower activation energy than that at dry air. The O-H bond and Fermi level shift of the YSZ thin film at wet air are observed by PES spectra. These results indicate that the YSZ thin film exhibits proton conduction at wet air in the temperature region of 300~500 ºC.