Reservoir computing in a lithium-based magneto-ionic device

Abstract

In-materio computing exploits the intrinsic physical dynamics of materials to perform complex computations, enabling low-power, real-time data processing by embedding computation directly within physical layers. Here, we demonstrate a voltage-controlled magneto-ionic device that functions as a reservoir computer capable of forecasting chaotic time series. The device consists of a crossbar structure with a Ta/CoFeB/Ta/MgO/Ta bottom electrode and a LiPON/Pt top electrode. A chaotic Mackey-Glass time series is encoded into a voltage signal applied to the device, while 2D Fourier transforms of voltage-dependent magnetic domain patterns form the output. Performance is influenced by the input rate, smoothing of the output, the number of elements in the reservoir state vector, and the training duration. We identify two distinct computational regimes: short-term prediction is optimized using smoothed, low-dimensional states with minimal training, whereas prediction around the Mackey-Glass delay time benefits from unsmoothed, high-dimensional states and extended training. Reservoir computing metrics reveal that slower input rates are more tolerant to output smoothing, while faster input rates degrade both memory capacity and nonlinear processing. These findings demonstrate the potential of magneto-ionic systems for neuromorphic computing and offer design principles for tuning performance in response to input signal characteristics.