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Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network
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,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8000076/ https://www.ncbi.nlm.nih.gov/pubmed/33807986 http://dx.doi.org/10.3390/mi12030268 |
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author | H Hasan, Mohammad Abbasalipour, Amin Nikfarjam, Hamed Pourkamali, Siavash Emad-Ud-Din, Muhammad Jafari, Roozbeh Alsaleem, Fadi |
author_facet | H Hasan, Mohammad Abbasalipour, Amin Nikfarjam, Hamed Pourkamali, Siavash Emad-Ud-Din, Muhammad Jafari, Roozbeh Alsaleem, Fadi |
author_sort | H Hasan, Mohammad |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8000076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80000762021-03-28 Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network H Hasan, Mohammad Abbasalipour, Amin Nikfarjam, Hamed Pourkamali, Siavash Emad-Ud-Din, Muhammad Jafari, Roozbeh Alsaleem, Fadi Micromachines (Basel) Article 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. MDPI 2021-03-05 /pmc/articles/PMC8000076/ /pubmed/33807986 http://dx.doi.org/10.3390/mi12030268 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article H Hasan, Mohammad Abbasalipour, Amin Nikfarjam, Hamed Pourkamali, Siavash Emad-Ud-Din, Muhammad Jafari, Roozbeh Alsaleem, Fadi Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network |
title | Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network |
title_full | Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network |
title_fullStr | Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network |
title_full_unstemmed | Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network |
title_short | Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network |
title_sort | exploiting pull-in/pull-out hysteresis in electrostatic mems sensor networks to realize a novel sensing continuous-time recurrent neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8000076/ https://www.ncbi.nlm.nih.gov/pubmed/33807986 http://dx.doi.org/10.3390/mi12030268 |
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