As AI devices become increasingly sophisticated, demand for energy-efficient, high-performance solutions continues to grow. The newly developed device generates spin waves using antennas integrated with yttrium iron garnet (YIG) magnets, a material critical to its operation. The interference patterns of these spin waves can be fine-tuned by applying voltage to the magnets and adjusting the number of ions introduced into them. The device is able to perform computations by leveraging these dynamic interference patterns through an iono-magnonic reservoir. This approach delivers performance far surpasses conventional physical reservoir computing devices.

This device demonstrated exceptional performance in time-series predictions, achieving error rates less than one tenth those of conventional devices. Its prediction accuracy was evaluated using a standard testing method based on the Mackey–Glass equations, which are commonly used to model complex variations in biological systems.

This technology can be implemented in both magnetic thin films and single crystals, while being miniaturized without performance degradation, making it potentially suitable for various industrial applications. When integrated with different sensors, it has the potential to enable energy-efficient, high-precision AI devices for a wide range of purposes.

This project was carried out by a research team consisting of Takashi Tsuchiya (Group Leader, Neuromorphic Devices Group (NDG), Research Center for Materials Nanoarchitectonics (MANA), NIMS), Wataru Namiki (Postdoctoral Researcher, NDG, MANA, NIMS at the time of this project; currently Researcher, MANA, NIMS), Daiki Nishioka (Trainee, NDG, MANA, NIMS at the time of this project; currently Research Fellow, International Center for Young Scientists, NIMS), Kazuya Terabe (Group Leader, Ionic Devices Group, MANA, NIMS), Yuki Nomura (Senior Researcher, Nanostructures Research Laboratory (NRL), JFCC) and Kazuo Yamamoto (Group Leader, NRL, JFCC). This work was supported by funding from the National Security Technology Research Promotion Fund of the Acquisition, Technology & Logistics Agency, Ministry of Defense.

This research was published in Advanced Science, an open access journal, on November 17, 2024.