谐振器
微电子机械系统
油藏计算
非线性系统
计算机科学
电子工程
材料科学
光电子学
工程类
物理
人工神经网络
人工智能
量子力学
循环神经网络
作者
Faizan Tariq Beigh,Yu-Chi Chuang,Priyanka Singh,Nadeem Tariq Beigh,Shashank Narain,Shreya Singla,Yi Chiu,Dhiman Mallick
标识
DOI:10.1109/edtm58488.2024.10511966
摘要
Reservoir Computing (RC) is a promising neuromorphic paradigm for future applications on the Internet of Things(IoT) and is characterized by low training costs and hardware compatibility. This study introduces a MEMS resonator as a nonlinear node for non-delayed RC(ND-RC), specifically tailored for processing time-varying data. To investigate the nonlinear dynamics of a clamped-clamped silicon beam subjected to electrostatic driving, this study employs both experimental and numerical analyses. By utilizing the critical Duffing nonlinear parameters of a single MEMS resonator, ND-RC is implemented, reducing processing delays and enhancing the system dimensionality. Parametric optimization of the RC operation was conducted through numerical analysis, demonstrating its feasibility. The resonator exhibited high-performance capabilities, as evidenced by its success in a handwritten digit recognition benchmark, surpassing the reported works.
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