Magneto-Ionic Physical Reservoir Computing

Published

December 9, 2024

Author(s)

Md Mahadi Rajib, Dhritiman Bhattacharya, Christopher Jensen, Gong Chen, Fahim F. Chowdhury, Shouvik Sarkar, Kai Liu, Jayasimha Atulashimha

Abstract

Recent progresses in magneto-ionics offer exciting potentials to leverage its non-linearity, short-term memory, and energy-efficiency to uniquely advance the field of physical reservoir computing. In this work, we experimentally demonstrate the classification of temporal data using a magneto-ionic heterostructure. The device was specifically engineered to induce non-linear ion migration dynamics, which in turn imparted non-linearity and short-term memory (STM) to the magnetization. These capabilities—key features for enabling reservoir computing—were investigated, and the role of the ion migration mechanism, along with its history dependence on STM, was explained. These attributes were utilized to distinguish between sine and square waveforms within a randomly distributed set of pulses. Additionally, two important performance metrics—short-term memory (STM) and parity check capacity (PC)—were quantified, yielding promising values of 1.44 and 2, respectively, comparable to those of other state-of-the-art reservoirs. Our work paves the way for exploiting the relaxation dynamics of solid-state magneto-ionic platforms and developing energy-efficient magneto-ionic reservoir computing devices.

Citation

Nature Communications

Pub Type

Journals

Keywords

Magneto-ionic, reservoir computing, short-term memory, electric control of magnetism

Citation

Mahadi Rajib, M. , Bhattacharya, D. , Jensen, C. , Chen, G. , Chowdhury, F. , Sarkar, S. , Liu, K. and Atulashimha, J. (2024), Magneto-Ionic Physical Reservoir Computing, Nature Communications (Accessed October 17, 2025)