Ion-gating Reservoirs Operating on Electric Double Layer and Redox Mechanisms for High Performance Physical Reservoir Computing

Takashi Tsuchiya^a, Daiki Nishioka^a^,^b, Wataru Namiki^a, Kaoru Shibata^a^,^b, Tomoki Wada^a^,^b, Makoto Takayanagi^a^,^b, Masataka Imura^a, Yasuo Koide^a, Tohru Higuchi^b, Kazuya Terabe^a

^aNational Institute for Materials Science, Japan, 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan

^bTokyo University of Science, Japan, Tokyo, Japan

Proceedings of 24th International Conference on Solid State Ionics (SSI24)

Devices for a Net Zero World

London, United Kingdom, 2024 July 14th - 19th

Organizers: John Kilner and Stephen Skinner

Oral, Takashi Tsuchiya, presentation 091
Publication date: 10th April 2024

Artificial neural network (ANN)-based computing [e.g., deep learning with a multi-layer neural network (NN)] 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 (as in a typical multi-layer NN) are far higher than that required by human beings. To overcome the low energy efficiency of ANN computing, in-materio computing, in which inherent properties of materials are harnessed to perform computing, has recently been attracting attention. Among various types of the computing, physical reservoir computing (PRC) is particularly attractive because of its ability to significantly reduce the computational resources required to process time-series data by utilization of the nonlinear responses of a ‘reservoir’ (material or device, as a dynamical system) to input signals. Chaotic dynamics (including edge of chaos dynamics) has been theoretically predicted to be advantageous for achieving high performance PRC, because of excellent nonlinearity and high dimensionality (diversity) of output, which enable mapping input to high dimensional feature space. Realizing chaotic dynamics with nanomaterials and/or nanospace is thus a great challenge for development of low power consumption and highly integrated ANN-based computing devices. Recently, we have developed high performance PRC devices on the basis of nanoionics principle [1-4]. One example is a novel PRC nanoionic device, ion-gating reservoir, which utilizes ion-electron coupled dynamics in the vicinity of solid electric double layer in the edge of chaos state [1,2]. Various time-series data processing including nonlinear autoregressive moving-average (NARMA) task, hand written digit recognition, and spoken digit recognition could be performed with high accuracy. Redox behaviors of ion-electron mixed conductors (e.g., Li-WOx, LiCoO2) have also been successfully applied to ion-gating reservoirs, leading to slow responses, which are advantageous for dealing events with time constants over several tens of seconds [3,4]. In the presentation, the performance of the devices in various computing tasks, and the mechanisms will be discussed.

References:

[1] Nishioka, D.; Tsuchiya, T.; Namiki, W.; Takayanagi, M.; Imura, M.; Koide, Y.; Higuchi, T.; Terabe, K. Edge-of-chaos learning achieved by ion-electron–coupled dynamics in an ion-gating reservoir. Sci. Adv. 2022, 8, eade1156.

[2] Takayanagi, M.; Nishioka, D.; Tsuchiya, T.; Imura, M.; Koide, Y.; Higuchi, T.; Terabe, K. Ultrafast-switching of an all-solid-state electric double layer transistor with a porous yttria-stabilized zirconia proton conductor and the application to neuromorphic computing. Mater. Today Adv. 2023, 18, 100393.

[3] Wada, T.; Nishioka, D.; Namiki, W.; Tsuchiya, T.; Higuchi, T.; Terabe, K. A Redox‐Based Ion‐Gating Reservoir, Utilizing Double Reservoir States in Drain and Gate Nonlinear Responses. Adv. Intell. Syst. 2023, 5, 2300123

[4] Shibata, K.; Nishioka, D.; Namiki, W.; Tsuchiya, T.; Higuchi, Tohru.; Terabe, K. Redox-based ion-gating reservoir consisting of (104) oriented LiCoO2 film, assisted by physical masking. Sci. Rep. 2023, 13, 21060.

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