Title

Exploiting pull-in/pull-out hysteresis in electrostatic mems sensor networks to realize a novel sensing continuous-time recurrent neural network

Document Type

Article

Publication Date

3-1-2021

Publication Title

Micromachines

Volume

12

Issue

3

First Page

1

Last Page

17

DOI

10.3390/mi12030268

Keywords

CTRNN, MEMS, Neuromorphic computing, Sensor Network

Abstract

The goal of this paper is to provide a novel computing approach that can be used to reduce the power consumption, size, and cost of wearable electronics. To achieve this goal, the use of microelectromechanical systems (MEMS) sensors for simultaneous sensing and computing is introduced. Specifically, by enabling sensing and computing locally at the MEMS sensor node and utilizing the usually unwanted pull in/out hysteresis, we may eliminate the need for cloud computing and reduce the use of analog-to-digital converters, sampling circuits, and digital processors. As a proof of concept, we show that a simulation model of a network of three commercially available MEMS accelerometers can classify a train of square and triangular acceleration signals inherently using pull-in and release hysteresis. Furthermore, we develop and fabricate a network with finger arrays of parallel plate actuators to facilitate coupling between MEMS devices in the network using actuating assemblies and biasing assemblies, thus bypassing the previously reported coupling challenge in MEMS neural networks.

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