A MEMS Nonlinear Dynamic Approach for Neural Computing

This paper presents a novel concept for a micro-electro-mechanical-system (MEMS) neural computing unit based on the neuron rate model theory that is the basis of the dynamic field theory; a qualitative neuron approach to model cognition and human behavior, and the continuous time recurrent neural networks (CTRNNs); a class of artificial neural network. The concept utilizes the nonlinear dynamics of MEMS resonators, specifically, bi-stability and hysteresis, to simulate the detection and memory of a single rate model neuron. We introduce bi-stability into a straight microbeam by actuating the microbeam with an AC voltage at a frequency near its electric circuit resonance, which also significantly amplifies the applied voltage and introduces electromechanical coupling. Moreover, the memory and detection processes have been demonstrated using a MEMS arch beam by utilizing snap-through instability. Finally, we demonstrate electrical coupling between multiple artificial MEMS neurons to produce the selection process. Single degree of freedom models and initial preliminary experimental data are presented to qualitatively compare the dynamics of bi-stable MEMS to that of a rate model neuron.