Few-molecule reservoir computing experimentally demonstrated with surface enhanced Raman scattering and ion-gating stimulation

TL;DR

This paper demonstrates a novel few-molecule reservoir computing system using surface enhanced Raman scattering and ion-gating, achieving high accuracy in pattern recognition and dynamic task solving with minimal molecular volume.

Contribution

It introduces a practical, small-scale molecular reservoir computing approach leveraging SERS and ion-gating, advancing neuromorphic computing with minimal physical reservoirs.

Findings

Achieved 95.1%-97.7% accuracy in nonlinear waveform transformations.

Solved a second-order nonlinear dynamic equation with 94.3% accuracy.

Demonstrated effective molecular computing with a few molecules.

Abstract

Reservoir computing (RC) is a promising solution for achieving low power consumption neuromorphic computing, although the large volume of the physical reservoirs reported to date has been a serious drawback in their practical application. Here, we report the development of a few-molecule RC that employs the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA) detected by surface enhanced Raman scattering (SERS) with tungsten oxide nanorod/silver nanoparticles (WOx@Ag-NPs). The Raman signals of the pMBA molecules, adsorbed at the SERS active site of WOx@Ag-NPs, were reversibly perturbated by the application of voltage-induced local pH changes in the vicinity of the molecules, and then used to perform RC of pattern recognition and prediction tasks. In spite of the small number of molecules employed, our system achieved good performance, including 95.1% to 97.7% accuracy in various nonlinear waveform transformations and 94.3% accuracy in solving a second-order nonlinear dynamic equation task. Our work provides a new concept of molecular computing with practical computation capabilities.