Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neural networks are hybridized in four different ways for noise reduction and accuracy improvement in MEMS gyro. These are two-layer homogenous recurrent networks built on long short term memory (LSTM-LSTM) and gated recurrent unit (GRU-GRU), respectively; and another two-layer but heterogeneous deep networks built on long short term memory-gated recurrent unit (LSTM-GRU) and a gated recurrent unit-long short term memory (GRU-LSTM). Practical implementation with static and dynamic experiments was carried out for a custom MEMS-IMU to validate the proposed networks, and the results show that GRU-LSTM seems to be overfitting large amount data testing for three-dimensional axis gyro in the static test. However, for X-axis and Y-axis gyro, LSTM-GRU had the best noise reduction effect with over 90% improvement in the three axes. For Z-axis gyroscope, LSTM-GRU performed better than LSTM-LSTM and GRU-GRU in quantization noise and angular random walk, while LSTM-LSTM shows better improvement than both GRU-GRU and LSTM-GRU networks in terms of zero bias stability. In the dynamic experiments, the Hilbert spectrum carried out revealed that time-frequency energy of the LSTM-LSTM, GRU-GRU, and GRU-LSTM denoising are higher compared to LSTM-GRU in terms of the whole frequency domain. Similarly, Allan variance analysis also shows that LSTM-GRU has a better denoising effect than the other networks in the dynamic experiments. Overall, the experimental results demonstrate the effectiveness of deep learning algorithms in MEMS gyro noise reduction, among which LSTM-GRU network shows the best noise reduction effect and great potential for application in the MEMS gyroscope area.

Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

This paper presents a method of simulating a capacitive sensor using COMSOL Multiphysics. A petal-form sensitive electrode structure applied to a capacitive angle sensor was simulated using COMSOL software; the angle was calculated using sines and cosines with capacitance value to fill in the simulation of the capacitive angle sensor. First, according to the construction principle of the sensor structure, a GeoGebra graphing calculator with theoretical analysis and expression of function was used to obtain the two-dimensional structure of the target petal-form sensitive structure. Second, the holistic structural model was completed by importing it into COMSOL Multiphysics and setting the electrical and mechanical rotation parameters, which realized the simulation of the rotation process. Finally, because errors were found, the structure design was improved by introducing an angle between the partition domain, and the simulation errors were reduced. The resulting curves and calculated angles were consistent with the theory; the simulation results showed that the maximum angle difference value was 0.0175° and the minimum angle difference value was 0.0018° of a single cycle structure.

This paper discusses the effect of mechanical installation errors on the c results of capacitive angle sensor by COMSOL Multiphysics software; the angle was calculated using sines and cosines with capacitance value of the capacitive angle sensor. First, based on the principle of the sensor structure, the formula is derived which is petal-shaped in polar coordinates, and set the electrical parameters and rotation angles to realize the model simulation. Secondly, due to the installation errors which are easy to occur in real life, the simulation results are obtained by tilting and moving the model body to imitating the installation error in different cases. Finally, the results of installation errors in different initial situations are further analyzed and compared, including different tilt angles and horizontal movement distances respectively, and summarizing the impact of different installation errors on the measurement results.

A 12-bit capacitor weight self-calibration successive approximation analog-to-digital converter (SAR ADC) is presented. The amplifier structure with Output-Offset Storage (OOS) technology is used to optimize circuitry to lower comparator offset voltage and noise. The bottom-plate sampling suppresses charge injection, thereby improving linearity and noise performance. The design also employs capacitive weight self-calibration with a proposed sign-aware two’s complement mapper, which enhances the ADC’s signal-to-noise-and-distortion ratio (SNDR) to 72.09 dB and demonstrates excellent conversion accuracy. The proposed ADCs were fabricated in a 0.18μm 1P6M CMOS process. It consumes 36.8 mW, occupies an active area of 1117μm×717μm using 1.8V supply and the sampling rate of 3 MS/s.