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Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer

This paper presents a deep neural network (DNN) based design optimization methodology for dual-axis microelectromechanical systems (MEMS) capacitive accelerometer. The proposed methodology considers the geometric design parameters and operating conditions of the MEMS accelerometer as input parameter...

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Autores principales: Mattoo, Fahad A., Nawaz, Tahir, Saleem, Muhammad Mubasher, Khan, Umar Shahbaz, Hamza, Amir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143555/
https://www.ncbi.nlm.nih.gov/pubmed/37421050
http://dx.doi.org/10.3390/mi14040817
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author Mattoo, Fahad A.
Nawaz, Tahir
Saleem, Muhammad Mubasher
Khan, Umar Shahbaz
Hamza, Amir
author_facet Mattoo, Fahad A.
Nawaz, Tahir
Saleem, Muhammad Mubasher
Khan, Umar Shahbaz
Hamza, Amir
author_sort Mattoo, Fahad A.
collection PubMed
description This paper presents a deep neural network (DNN) based design optimization methodology for dual-axis microelectromechanical systems (MEMS) capacitive accelerometer. The proposed methodology considers the geometric design parameters and operating conditions of the MEMS accelerometer as input parameters and allows to analyze the effect of the individual design parameters on the output responses of the sensor using a single model. Moreover, a DNN-based model allows to simultaneously optimize the multiple output responses of the MEMS accelerometers in an efficient manner. The efficiency of the proposed DNN-based optimization model is compared with the design of the computer experiments (DACE) based multiresponse optimization methodology presented in the Literature, which showed a better performance in terms of two output performance metrics, i.e., mean absolute error (MAE) and root mean squared error (RMSE).
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spelling pubmed-101435552023-04-29 Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer Mattoo, Fahad A. Nawaz, Tahir Saleem, Muhammad Mubasher Khan, Umar Shahbaz Hamza, Amir Micromachines (Basel) Article This paper presents a deep neural network (DNN) based design optimization methodology for dual-axis microelectromechanical systems (MEMS) capacitive accelerometer. The proposed methodology considers the geometric design parameters and operating conditions of the MEMS accelerometer as input parameters and allows to analyze the effect of the individual design parameters on the output responses of the sensor using a single model. Moreover, a DNN-based model allows to simultaneously optimize the multiple output responses of the MEMS accelerometers in an efficient manner. The efficiency of the proposed DNN-based optimization model is compared with the design of the computer experiments (DACE) based multiresponse optimization methodology presented in the Literature, which showed a better performance in terms of two output performance metrics, i.e., mean absolute error (MAE) and root mean squared error (RMSE). MDPI 2023-04-04 /pmc/articles/PMC10143555/ /pubmed/37421050 http://dx.doi.org/10.3390/mi14040817 Text en © 2023 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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mattoo, Fahad A.
Nawaz, Tahir
Saleem, Muhammad Mubasher
Khan, Umar Shahbaz
Hamza, Amir
Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer
title Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer
title_full Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer
title_fullStr Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer
title_full_unstemmed Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer
title_short Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer
title_sort deep learning based multiresponse optimization methodology for dual-axis mems accelerometer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143555/
https://www.ncbi.nlm.nih.gov/pubmed/37421050
http://dx.doi.org/10.3390/mi14040817
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