Artificial neural network (ANN)-based computing can provide excellent learning, classification, and inference characteristics that are close to, and in some cases beyond, those found in natural intelligence (i.e., the human brain), whereas the enormous amounts of power required by ANN are far higher than that required by human beings. To overcome the low energy efficiency of ANN computing, physical reservoir computing (PRC) is particularly attractive because it can significantly reduce the computational resources required to process time-series data by leveraging the nonlinear responses of a ‘reservoir’ (a material or device acting as a dynamical system) to input signals. Recently, we have developed high-performance PRC devices based on iontronic phenomena. One example is an ion-gating reservoir (IGR), which utilizes ion-electron coupled dynamics in the vicinity of a solid electric double layer at the diamond/Li+ solid electrolyte interface [1]. The edge-of-chaos state of the IGR enabled the best computational capacity. Furthermore, the IGR, consisting of graphene and ion-gel, demonstrates an exceptionally broad responsive range, from 1 MHz to 20 Hz, while maintaining a high information processing capacity and adaptability across multiple time scales [2]. The IGR achieved deep learning (DL)-level accuracy in chaotic time series prediction tasks while reducing computational resource requirements to 1/100 of those needed by DL. Another example is a magnonic PRC device that utilizes the high-speed nonlinear dynamics of interfered spin waves in a ferrimagnetic Y3Fe5O12 single crystal [3].

The high PRC performance of the IGR is particularly advantageous for application to in-sensor computing, which is a system-level paradigm in which the sensor element itself not only converts an external stimulus into an electrical signal but also carries out local data processing (e.g., feature extraction, thresholding, logic, neuromorphic operations) on that signal [4]. One example is a real-time spoken-digit recognition system that directly processes throat vibrations by integrating a π–gel-electret mechano-electric generator (MEG) sensor with a multi-channel ion-gel/graphene IGR [5]. This hybrid MEG–IGR system achieves an impressive real-time spoken-digit recognition accuracy of 96.8%, demonstrating that IGR can efficiently extract discriminative spatiotemporal features from raw biomechanical signals. Another example is a high-speed gas classification with a membrane-type surface stress sensor. The IGR, with its broad responsive range and small volume, can serve as a versatile platform for high-performance in-sensor computing with various sensors.