Dual-Mass MEMS Gyroscope Structure, Design, and Electrostatic Compensation

Cao, Huiliang; Li, Jianhua

doi:10.5772/intechopen.74364

Cited by 2 publications

(1 citation statement)

References 31 publications

(59 reference statements)

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“…Another approach is using temperature compensation methods that can correct the output error due to temperature variation [9]. A dual mass MEMS gyroscope structure with electrostatic compensation was presented to reduce the quadrature error and bandwidth was expended from 13 to 104 Hz [14].…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of mode-matched decoupled mass MEMS gyroscope with improved thermal stability

Bashir,

Bazaz,

Saleem

et al. 2024

Meas. Sci. Technol.

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This paper presents an efﬁcient design approach for microelectromechanical systems (MEMS) gyroscope considering the design constraint of MEMSCAP's Silicon-on-Insulator Multi-User MEMS Processes (SOIMUMPs). A design for a decoupled mass MEMS resonant gyroscope with tuning electrodes is presented to minimize cross-axis coupling and optimize the gyroscope's performance. The device features a size of 1.8 ×2.772 mm^2. A decoupled mass MEMS gyroscope has been designed using a novel mechanical beam configuration that optimizes the structural design to reduce unwanted interactions between drive and sense axes motions. Mode order has been achieved by obtaining the first two modes as drive and sense modes at 9946.3 Hz and 10202.7 Hz, respectively. Parallel plate electrostatic tuning electrodes have been added to adjust and match the drive and sense modes at the resonant frequencies of 9946 Hz. This mode matching is crucial for optimizing gyroscope performance, accuracy, and sensitivity. The effects of temperature variation on the performance of the designed decoupled mass MEMS gyroscope have been investigated, particularly in terms of its mode-matched operation achieved using tuning electrodes. Finite Element Method (FEM) simulations were conducted, demonstrating that thermal stability was achieved, and mode-matched operation was maintained within the temperature range of -40 ºC to 80 ºC. The gyroscope exhibits enhanced performance compared to existing MEMS gyroscopes under mode-matched conditions, featuring a lower temperature coefficient of frequency (TCF) at 0.0415 Hz/ºC in the drive mode and 0.0165 Hz/ºC in the sense mode. FEM analysis for the fabrication process tolerances has been done, where MEMS gyroscope’s resonant frequencies, output capacitance, output displacement, and sense mode quality factor have shown negligible changes to fabrication process tolerances. Transient analysis was conducted using CoventorWare MEMS+ 3D schematic, which was integrated with MATLAB Simulink to measure the MEMS gyroscope’s output response, which corresponded with the varying input angular velocity signal.

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Section: Introductionmentioning

confidence: 99%

Design and analysis of mode-matched decoupled mass MEMS gyroscope with improved thermal stability

Bashir,

Bazaz,

Saleem

et al. 2024

Meas. Sci. Technol.

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An Improved GPS/INS Integration Based on EKF and AI During GPS Outages

Ebrahimi

Nezhadshahbodaghi

Mosavi

et al. 2023

J CIRCUIT SYST COMP

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Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at [Formula: see text] and [Formula: see text]. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method.

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Dual-Mass MEMS Gyroscope Structure, Design, and Electrostatic Compensation

Cited by 2 publications

References 31 publications

Design and analysis of mode-matched decoupled mass MEMS gyroscope with improved thermal stability

Design and analysis of mode-matched decoupled mass MEMS gyroscope with improved thermal stability

An Improved GPS/INS Integration Based on EKF and AI During GPS Outages

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