In recent years, the increasing power consumption of artificial intelligence (AI) has become a critical challenge, driving growing interest in novel computing paradigms inspired by the highly efficient information processing mechanisms observed in biological and neural systems. One promising approach is physical reservoir computing (PRC), which leverages the inherent spatiotemporal dynamics of materials and devices as a virtual neural network to process information. By exploiting the complex physical properties of various systems, PRC offers a fundamentally different approach to computation compared to conventional von Neumann architectures. Although PRC has been demonstrated in various physical systems[1,2], several key challenges remain unaddressed, preventing its practical implementation. These challenges include low computational performance, a narrow operational speed range, and the relatively large size of PRC devices, which hinder their scalability and integration into practical applications. Overcoming these limitations requires a novel strategy to fully harness the computational potential inherent in materials based on nanotechnology. To address this issue, we have developed ion-gating reservoirs (IGRs), an innovative PRC framework that utilizes the modulation of material properties via ion-gating as a computational resource. IGRs are transistor-structured devices composed of an ionic conductor and a semiconductor, enabling highly efficient and adaptive information processing. By applying this concept to a variety of material systems, we successfully demonstrated high-performance reservoir computing based on electric double-layer (EDL) effects, redox reactions, and molecular vibration modulations [2-6]. These mechanisms introduce dynamic and nonlinear responses, significantly enhancing the computational capacity of the reservoir.

In this presentation, we report on the development of IGRs across various material platforms and highlight the latest findings on ion-gel/graphene-based IGRs [7]. In this system, the interplay of multiple physical effects—including EDL-induced carrier injection, graphene ambipolar behavior, and charge trapping dynamics—gives rise to intricate spatiotemporal state evolution. This behavior is expected to contribute to network characteristics analogous to those found in biological neural systems, thereby enhancing the system’s computational versatility. To quantitatively assess the computational performance of our IGR, we conducted a benchmark task based on the Mackey-Glass equation, a well-known chaotic time-series prediction problem frequently used in machine learning evaluations. Our IGR exhibited prediction accuracy on par with deep learning (DL) models while achieving a two-order-of-magnitude reduction in computational load, highlighting its efficiency. Furthermore, we found that our system reached or even exceeded the theoretical computational efficiency limit observed in well-optimized simulation-based reservoir computing models, demonstrating its remarkable performance. These findings underscore the exceptional efficiency and computational potential of IGRs, positioning them as a promising candidate for next-generation computing technologies. By harnessing the complex dynamics of materials induced by ion-gating, IGRs provide a powerful platform for neuromorphic computing, which seeks to emulate the highly efficient and adaptive information processing observed in biological and neural systems. This work represents a significant step toward realizing energy-efficient, high-performance computing architectures inspired by nature, with potential applications in real-time signal processing, machine learning, and brain-inspired artificial intelligence.