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An amendment to this paper has been published and can be accessed via a link at the top of the paper.

This paper reports the design, fabrication, and characterization of a MEMS capacitive accelerometer with an asymmetrical comb finger arrangement. By optimizing the ratio of the gaps of a rotor finger to its two adjacent stator fingers, the sensitivity of the accelerometer is maximized for the same comb finger area. With the fingers' length, width, and depth at 120 μm, 4 μm, and 45 μm, respectively, the optimized finger gap ratio is 2.5. The area of the proof mass is 750 μm × 560 μm, which leads to a theoretical thermomechanical noise of 9 μg/√Hz. The accelerometer has been fabricated using a modified silicon-on-glass (SOG) process, in which a groove is pre-etched into the glass to hold the metal electrode. This SOG process greatly improves the silicon-to-glass bonding yield. The measurement results show that the resonant frequency of the accelerometer is about 2.05 kHz, the noise floor is 28 μg/√Hz, and the nonlinearity is less than 0.5%. [ABSTRACT FROM AUTHOR]

The noise signal in the gyroscope is divided into four levels: sampling frequency level, device bandwidth frequency level, resonant frequency level, and carrier frequency level. In this paper, the signal in the dual-mass MEMS gyroscope is analyzed. Based on the variational mode decomposition (VMD) algorithm, a novel dual-mass MEMS gyroscope noise reduction method is proposed. The VMD method with different four-level center frequencies is used to process the original output signal of the MEMS gyroscope, and the results are analyzed by the Allan analysis of variance, which shows that the ARW of the gyroscope is increased from 1.998*10−1°/√h to 1.552*10−4°/√h, BS increased from 2.5261°/h to 0.0093°/h.

This study proposes a fusion algorithm based on forward linear prediction (FLP) and particle swarm optimization–back propagation (PSO-BP) to compensate for the temperature drift. Firstly, the accelerometer signal is broken down into several intrinsic mode functions (IMFs) using variational modal decomposition (VMD); then, according to the FE algorithm, the IMF signal is separated into mixed components, temperature drift, and pure noise. After that, the mixed noise is denoised by FLP, and PSO-BP is employed to create a model for temperature adjustment. Finally, the processed mixed noise and the processed IMFs are rebuilt to obtain the enhanced output signal. To confirm that the suggested strategy works, temperature experiments are conducted. After the output signal is processed by the VMD-FE-FLP-PSO-BP algorithm, the acceleration random walk has been improved by 23%, the zero deviation has been enhanced by 24%, and the temperature coefficient has been enhanced by 92%, compared with the original signal. [ABSTRACT FROM AUTHOR]

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope's working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time–frequency peak filtering, non-dominated sorting genetic algorithm-II (NSGA II) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope's output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time–frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA II is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope's output signal is analyzed based on Allen variance; its angle random walk is decreased from 0.531076°/h/√Hz to 6.65894 × 10−3°/h/√Hz and its bias stability is decreased from 32.7364°/h to 0.259247°/h. [ABSTRACT FROM AUTHOR]