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Machine Learning-Based Modeling and Generic Design Optimization Methodology for Radio-Frequency Microelectromechanical Devices
RF-MEMS technology has evolved significantly over the years, during which various attempts have been made to tailor such devices for extreme performance by leveraging novel designs and fabrication processes, as well as integrating unique materials; however, their design optimization aspect has remai...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143628/ https://www.ncbi.nlm.nih.gov/pubmed/37112340 http://dx.doi.org/10.3390/s23084001 |
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author | Bajwa, Rayan Yapici, Murat Kaya |
author_facet | Bajwa, Rayan Yapici, Murat Kaya |
author_sort | Bajwa, Rayan |
collection | PubMed |
description | RF-MEMS technology has evolved significantly over the years, during which various attempts have been made to tailor such devices for extreme performance by leveraging novel designs and fabrication processes, as well as integrating unique materials; however, their design optimization aspect has remained less explored. In this work, we report a computationally efficient generic design optimization methodology for RF-MEMS passive devices based on multi-objective heuristic optimization techniques, which, to the best of our knowledge, stands out as the first approach offering applicability to different RF-MEMS passives, as opposed to being customized for a single, specific component. In order to comprehensively optimize the design, both electrical and mechanical aspects of RF-MEMS device design are modeled carefully, using coupled finite element analysis (FEA). The proposed approach first generates a dataset, efficiently spanning the entire design space, based on FEA models. By coupling this dataset with machine-learning-based regression tools, we then generate surrogate models describing the output behavior of an RF-MEMS device for a given set of input variables. Finally, the developed surrogate models are subjected to a genetic algorithm-based optimizer, in order to extract the optimized device parameters. The proposed approach is validated for two case studies including RF-MEMS inductors and electrostatic switches, in which the multiple design objectives are optimized simultaneously. Moreover, the degree of conflict among various design objectives of the selected devices is studied, and corresponding sets of optimal trade-offs (pareto fronts) are extracted successfully. |
format | Online Article Text |
id | pubmed-10143628 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101436282023-04-29 Machine Learning-Based Modeling and Generic Design Optimization Methodology for Radio-Frequency Microelectromechanical Devices Bajwa, Rayan Yapici, Murat Kaya Sensors (Basel) Article RF-MEMS technology has evolved significantly over the years, during which various attempts have been made to tailor such devices for extreme performance by leveraging novel designs and fabrication processes, as well as integrating unique materials; however, their design optimization aspect has remained less explored. In this work, we report a computationally efficient generic design optimization methodology for RF-MEMS passive devices based on multi-objective heuristic optimization techniques, which, to the best of our knowledge, stands out as the first approach offering applicability to different RF-MEMS passives, as opposed to being customized for a single, specific component. In order to comprehensively optimize the design, both electrical and mechanical aspects of RF-MEMS device design are modeled carefully, using coupled finite element analysis (FEA). The proposed approach first generates a dataset, efficiently spanning the entire design space, based on FEA models. By coupling this dataset with machine-learning-based regression tools, we then generate surrogate models describing the output behavior of an RF-MEMS device for a given set of input variables. Finally, the developed surrogate models are subjected to a genetic algorithm-based optimizer, in order to extract the optimized device parameters. The proposed approach is validated for two case studies including RF-MEMS inductors and electrostatic switches, in which the multiple design objectives are optimized simultaneously. Moreover, the degree of conflict among various design objectives of the selected devices is studied, and corresponding sets of optimal trade-offs (pareto fronts) are extracted successfully. MDPI 2023-04-14 /pmc/articles/PMC10143628/ /pubmed/37112340 http://dx.doi.org/10.3390/s23084001 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 Bajwa, Rayan Yapici, Murat Kaya Machine Learning-Based Modeling and Generic Design Optimization Methodology for Radio-Frequency Microelectromechanical Devices |
title | Machine Learning-Based Modeling and Generic Design Optimization Methodology for Radio-Frequency Microelectromechanical Devices |
title_full | Machine Learning-Based Modeling and Generic Design Optimization Methodology for Radio-Frequency Microelectromechanical Devices |
title_fullStr | Machine Learning-Based Modeling and Generic Design Optimization Methodology for Radio-Frequency Microelectromechanical Devices |
title_full_unstemmed | Machine Learning-Based Modeling and Generic Design Optimization Methodology for Radio-Frequency Microelectromechanical Devices |
title_short | Machine Learning-Based Modeling and Generic Design Optimization Methodology for Radio-Frequency Microelectromechanical Devices |
title_sort | machine learning-based modeling and generic design optimization methodology for radio-frequency microelectromechanical devices |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143628/ https://www.ncbi.nlm.nih.gov/pubmed/37112340 http://dx.doi.org/10.3390/s23084001 |
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