In-Materio Reservoir Computing Utilizing Spatiotemporal Dynamics of Ion, Electron, and Spin

Takashi Tsuchiya^a^,^b, Daiki Nishioka^a^,^c, Wataru Namiki^a, Ryo Iguchi^a, Yoshitaka Shingaya^a, Kazuya Terabe^a

^aResearch Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS)

^bDepartment of Applied physics, Tokyo University of Science

^cInternational Center for Young Scientists (ICYS), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba,, Ibaraki, Japan

Proceedings of MATSUS Spring 2026 Conference (MATSUSSpring26)

H4 Neuromorphic devices and systems

Barcelona, Spain, 2026 March 23rd - 27th

Organizer: Francesca Borghi

Invited Speaker, Takashi Tsuchiya, presentation 373
Publication date: 15th December 2025

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, which harnesses the inherent properties of materials to perform computation, has recently attracted attention. Among various types of 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. Realizing nonlinear and diverse dynamics with nanomaterials and/or nanospace is thus a major challenge for the development of low-power, highly integrated ANN-based computing devices. 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 [1,2]. Another example is a magnonic PRC device that utilizes the chaotic dynamics of interfered spin waves in a ferrimagnetic Y3Fe5O12 (YIG) single crystal [3]. In the presentation, the device performance and the operating mechanisms of the two PRC systems will be discussed.

References:

[1] [1]D. Nishioka et al., Edge-of-chaos learning achieved by ion-electron–coupled dynamics in an ion-gating reservoir. Sci. Adv. 8, eade1156(2022).

[2] [2]D. Nishioka et al., Two Orders of Magnitude Reduction in Computational Load Achieved by Ultrawideband Responses of an Ion-Gating Reservoir. ACS Nano. 19, 42, 36896–36914(2025).

[3] [3]W. Namiki et al., Experimental Demonstration of High-Performance Physical Reservoir Computing with Nonlinear Interfered Spin Wave Multidetection. Adv. Intell. Syst. 2300228(2023).

Acknowledgements:

This work was supported by Japan Science and Technology Agency (JST) as part of PRESTO (Grant number JPMJPR23H4) and Adopting Sustainable Partnerships for Innovative Research Ecosystem (ASPIRE).

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