Publication date: 15th April 2025
Physical reservoir computing, in which physical phenomena exhibiting nonlinear responses are used as reservoirs, is expected to be applied to next-generation edge AI devices because of its low learning cost and highly efficient processing. Ion gating reservoirs using high-density carrier injection with solid electrolytes have been realized in various material systems [1-4]. The surface-enhanced Raman scattering enables physical reservoir computing using Raman scattering signals from a single molecule as reservoir states, which can be used to predict changes in blood glucose levels with high accuracy[4]. In this study, we fabricated an electric double-layer transistor using MoS2 as a channel material on a solid electrolyte substrate and realized a MoS2-based Raman-ion-gating reservoir (MoS2-RIGR) that uses both Raman scattering signals and current responses in the channel region as computational resources.
Figure 1a shows schematic diagram of the fabricated MoS2-RIGR device. Multilayer MoS2 fabricated by the exfoliation method was used as the channel material. Figure 1b shows gate voltage dependence of Raman scattering spectra obtained from MoS2 channel. Here we use an excitation laser beam with a wavelength of 632.8 nm, which provides resonant Raman scattering effects for multilayer MoS2. The nonlinear waveform transformation task was performed using this Raman scattering signal, drain current, and gate current as reservoir states. As shown in Figure 1c, higher accuracy was obtained for all waveform transformations when both Raman and current responses were used as reservoir states. It was also found that the normalized mean squared error was reduced by 37% by including the Raman signal in the reservoir state in solving the second-order nonlinear dynamical equation.
This research was in part supported by JSPS KAKENHI Grant Number JP24KJ0229 (Grant-in-Aid for JSPS Fellows). A part of this work was supported by JST PRESTO Grant number, JPMJPR23H4.
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