Implementation of Reservoir Computing Using Coupled Microelectromechanical Drum Resonators via Sideband-Pumped Phonon-Cavity Dynamics
Theresa Farah, Lo\"ic Flis, Pierre Laly, Guo-En Chang, Jun-Yu Ou, Yoshishige Tsuchiya, Yan Pennec, Bahram Djafari-Rouhani, and Xin Zhou

TL;DR
This paper demonstrates a novel reservoir computing platform using coupled microelectromechanical drum resonators, leveraging phonon-cavity dynamics and sideband pumping to achieve nonlinear energy transfer for temporal information processing.
Contribution
It introduces a physical reservoir computing system based on coupled MEMS resonators with a sideband-pumped phonon-cavity scheme, enabling integrated sensing and computing.
Findings
Successful experimental implementation of MEMS-based reservoir computing.
Use of sideband pumping to induce nonlinear dynamics in coupled resonators.
Achieved benchmark performance with parity and NARMA tasks.
Abstract
Reservoir computing is a bio-inspired machine learning paradigm that exploits the intrinsic dynamics of nonlinear systems with fading memory for efficient temporal information processing. Microelectromechanical resonators offer a promising platform for reservoir computing as they inherently possess the requisite nonlinear and temporal properties while also facilitating the integration of sensing and computing within a single platform. In this work, we experimentally demonstrate a physical reservoir computing platform based on two capacitively coupled drum resonators, operating in the MHz frequency regime. Taking advantage of the concept of phonon-cavity electromechanics, a pump tone is applied at the sideband of the phonon cavity while probing one of the coupled modes, analogous to optomechanical systems, thereby creating nonlinear dynamics in energy transfer between the two resonators.…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Mechanical and Optical Resonators · Model Reduction and Neural Networks
