Neural Network Methods in the Development of MEMS Sensors
As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabri...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/15/11/1368 |
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| author | Yan Liu Mingda Ping Jizhou Han Xiang Cheng Hongbo Qin Weidong Wang |
| author_facet | Yan Liu Mingda Ping Jizhou Han Xiang Cheng Hongbo Qin Weidong Wang |
| author_sort | Yan Liu |
| collection | DOAJ |
| description | As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area. |
| format | Article |
| id | doaj-art-fb4db3ed786345e894b942dcad983bff |
| institution | Kabale University |
| issn | 2072-666X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj-art-fb4db3ed786345e894b942dcad983bff2024-11-26T18:13:59ZengMDPI AGMicromachines2072-666X2024-11-011511136810.3390/mi15111368Neural Network Methods in the Development of MEMS SensorsYan Liu0Mingda Ping1Jizhou Han2Xiang Cheng3Hongbo Qin4Weidong Wang5School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, ChinaAs a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area.https://www.mdpi.com/2072-666X/15/11/1368MEMS sensorneural networkstructural designfabricationcompensationcalibration |
| spellingShingle | Yan Liu Mingda Ping Jizhou Han Xiang Cheng Hongbo Qin Weidong Wang Neural Network Methods in the Development of MEMS Sensors Micromachines MEMS sensor neural network structural design fabrication compensation calibration |
| title | Neural Network Methods in the Development of MEMS Sensors |
| title_full | Neural Network Methods in the Development of MEMS Sensors |
| title_fullStr | Neural Network Methods in the Development of MEMS Sensors |
| title_full_unstemmed | Neural Network Methods in the Development of MEMS Sensors |
| title_short | Neural Network Methods in the Development of MEMS Sensors |
| title_sort | neural network methods in the development of mems sensors |
| topic | MEMS sensor neural network structural design fabrication compensation calibration |
| url | https://www.mdpi.com/2072-666X/15/11/1368 |
| work_keys_str_mv | AT yanliu neuralnetworkmethodsinthedevelopmentofmemssensors AT mingdaping neuralnetworkmethodsinthedevelopmentofmemssensors AT jizhouhan neuralnetworkmethodsinthedevelopmentofmemssensors AT xiangcheng neuralnetworkmethodsinthedevelopmentofmemssensors AT hongboqin neuralnetworkmethodsinthedevelopmentofmemssensors AT weidongwang neuralnetworkmethodsinthedevelopmentofmemssensors |