A Simplified Model MEMS Gyroscope Zero Bias Temperature Drift Calibration Method Based on BP Neural Network

Wang, Tong; Liu, Xi; Zhong, Sheng; Luo, Hangzai

doi:10.1109/icfeict57213.2022.00034

2022 2nd International Conference on Frontiers of Electronics, Information and Computation Technologies (ICFEICT) 2022

DOI: 10.1109/icfeict57213.2022.00034

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A Simplified Model MEMS Gyroscope Zero Bias Temperature Drift Calibration Method Based on BP Neural Network

Tong Wang

Xi Liu

Sheng Zhong

et al.

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2024

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Cited by 2 publications

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Self-calibration of hemispherical resonator gyroscopes under drastic changes of ambient temperature based on mode reversal

Zeng,

Deng,

Pan

et al. 2024

Sensors and Actuators A: Physical

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Self-calibration of hemispherical resonator gyroscopes under drastic changes of ambient temperature based on mode reversal

Zeng,

Deng,

Pan

et al. 2024

Sensors and Actuators A: Physical

View full text Add to dashboard Cite

Neural Network Methods in the Development of MEMS Sensors

Liu,

Ping,

Han

et al. 2024

Micromachines

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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.

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A Simplified Model MEMS Gyroscope Zero Bias Temperature Drift Calibration Method Based on BP Neural Network

Cited by 2 publications

References 13 publications

Self-calibration of hemispherical resonator gyroscopes under drastic changes of ambient temperature based on mode reversal

Self-calibration of hemispherical resonator gyroscopes under drastic changes of ambient temperature based on mode reversal

Neural Network Methods in the Development of MEMS Sensors

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