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.

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 proposes two wavelength-extended InGaAs p-type semiconductor - intrinsic absorption layer - n-type semiconductor structures to achieve methane detection at low temperatures. The responsivity of the strained and metamorphic structures is 0.98 and 1.09 A/W at 20 °C. The strained structure detector can achieve a response level of 0.7 A/W in low-temperature environments above −10 °C. The metamorphic structure shows a responsivity not less than 0.8 A/W within the temperature span of −40 to 85 °C. The dark current of both devices is at the nA level at −10 V. The saturation optical power of the strained structure and the metamorphic structure is 13 and 17 mW at −0.5 V, respectively. The cut-off wavelength is 1690 and 1730 nm for the strained structure and the metamorphic structure. These two structures tested by x-ray diffraction have high crystalline quality. The strained structure has a root mean square roughness of 0.13 nm, while the metamorphic structure's roughness is 0.39 nm. These results suggest that both strained and metamorphic structures have promising applications in low-temperature probes.