Iono-Magnonic Reservoir Computing Utilizing Interfered Spin Wave Manipulated By Ion-Gating
Authors: Namiki Wataru , Nishioka Daiki , Nomura Yuki , Yamamoto Kazuo , Terabe Kazuya , Tsuchiya Takashi 
Published: December 2024

Physical reservoir computing is a promising candidate for implementing high-performance artificial intelligence devices, mimicking biological system by using nonbiological components. The reservoir computing network has fewer learning parameters than conventional deep learning, and the advantage leads to high-speed processing and low electric power consumption. Since the physical reservoir plays a vital role in mapping input information depending on past state nonlinearly into a high dimensional space, the physical device must possess nonlinearity, short-term memory, and high dimensionality. However, some issues (i.e., high electrical power consumption, insufficient computational performance, and large volumes) still need to be addressed in achieving practical physical reservoir.

Our previous work revealed that nonlinear interfered spin wave multi-detection exhibits high computational performance on a second-order nonlinear autoregressive moving average (NARMA2) task.[1] While the spin wave is a Joule-loss-free information carrier, and its interference gives a reservoir rich expressive power (e.g., chaos), the computational performance is still inferior to an optoelectrical reservoir. The best way to overcome this problem is to manipulate the spin wave in situ.

Herein, we fabricated an iono-magnonic reservoir device to let the research fields of ionics and magnonics collaborate. This scheme is built on the basis of ion-gating, which can drastically manipulate magnetic property through redox reaction triggered by gate voltage (V
G) application.[2,3] This study is the first study of manipulating chaotic spin wave interference as the information carrier, achieved with a solid-state electrolyte and its application for high-performance reservoir computing.[4]

Figure A shows the fabricated physical reservoir, which consists of a Y3Fe5O12 (YIG) single crystal and a proton-conducting Nafion, and its measurement configuration. Two exciters for interference and two detectors for multi-detection are deposited on the YIG. Protons in Nafion migrate to the YIG by V
G application, and in situ magnetic property manipulation due to electron doping is expected.

Figure B shows the depth dependence of energy loss measured by electron energy-loss spectroscopy. While the energy loss of a Nafion/YIG to which V
G was applied ('biased') at the bulk region was in good agreement with that of a Nafion/YIG to which V
G was not applied (‘unbiased’), the energy loss at the Nafion/YIG interface region shifted to the lower energy side, indicating that Fe ion near the interface was electrochemically reduced from trivalent to divalent states. V
G dependence of M
S and H
a is shown in Fig. C. M
S of 1984.6 Gauss at V
G = 0.0 V is in good agreement with M
S of 1984.0 Gauss obtained from magnetization measurement in a pristine YIG single crystal. H
a at V
G = 0.0 V is 1586.7 Oe. Increasing V
G, corresponding to electron doping, decreases both M
S and H
a. The change saturates at the region of V
G ≥ 1.6 V. Spin wave frequency variations at various V
G are summarized in Fig. D. The frequencies under magnetic fields of 170 mT increase from approximately 1.3 GHz to 1.32 GHz, corresponding to a shift of 17.9 MHz, and the increase ratios are 1.38 %. This modulated spin wave can achieve a variety of reservoirs and may contribute to improving the computational performance.

Figure E shows a schematic concept of reservoir computing. The network was constructed by reservoir layers connected in parallel by utilizing the spin wave property modulated by ion-gating. Input time-series data is transformed nonlinearly to waveforms by 800 nodes (X
1 - X
800) in reservoir layers connected in parallel through utilizing spin waves modulated by eight V
G states. Then, these nodes are crossed by 800 output weights
W

out to generate a reservoir output. The computational error of the NARMA2 task is 9.53 x 10-3, which corresponds to reducing 47.3 %, compared to the computational performance of the nonlinear interfered spin wave multi-detection, and the computational error is much lower than that of the reservoir utilizing the optoelectronic system, as shown in Fig. F. This drastic improvement results from excellent nonlinearity of the chaotic spin wave interference and the ability to map in higher dimensional space by ion-gating, and the iono-magnonic reservoir updated the best performance in other physical reservoirs reported thus far. The iono-magnonic reservoir leads to achievement in high-performance artificial intelligence devices.

This work was partially supported by Innovative Science and Technology Initiative for Security Grant Number JPJ004596, ATLA, Japan, JST PRESTO (JPMJPR23H4), and JSPS KAKENHI Grant Numbers JP22H04625 and JP19H05814 (Grant-in-Aid for Scientific Research on Innovative Areas “Interface Ionics”).

References

[1] Namiki et al., Adv. Intell.
Syst.
5, 2300228 (2023).

[2] Namiki et al., Nano Lett.
24, 4383 (2024).

[3] Namiki et al., ACS Nano
14, 16065 (2020).

[4] Namiki et al., Adv. Sci. in revision.

Figure 1