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.

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.

An all-solid-state redox device, composed of magnetite (Fe 3 O 4 ) thin film and Li + conducting electrolyte thin film, was fabricated for the manipulation of a magnetization angle at room temperature (RT). This is a key technology for the creation of efficient spintronics devices, but has not yet been achieved at RT by other carrier doping methods. Variations in magnetization angle and magnetic stability were precisely tracked through the use of planar Hall measurements at RT. The magnetization angle was reversibly manipulated at 10°by maintaining magnetic stability. Meanwhile, the manipulatable angle reached 56°, although the manipulation became irreversible when the magnetic stability was reduced. This large manipulation of magnetic angle was achieved through tuning of the 3d electron number and modulation of the internal strain in the Fe 3 O 4 due to the insertion of high-density Li + (approximately 10 21 cm −3 ). This RT manipulation is applicable to highly integrated spintronics devices due to its simple structure and low electric power consumption.

No abstract