Temperature Drift Compensation of a MEMS Accelerometer Based on DLSTM and ISSA

Abstract

In order to improve the performance of a micro-electro-mechanical system (MEMS) accelerometer, three algorithms for compensating its temperature drift are proposed in this paper, including deep long short-term memory recurrent neural network (DLSTM-RNN, short DLSTM), DLSTM based on sparrow search algorithm (SSA), and DLSTM based on improved SSA (ISSA). Moreover, the piecewise linear approximation (PLA) method is employed in this paper as a comparison to evaluate the impact of the proposed algorithm. First, a temperature experiment is performed to obtain the MEMS accelerometer’s temperature drift output (TDO). Then, we propose a real-time compensation model and a linear approximation model for neural network methods compensation and PLA method compensation, respectively. The real-time compensation model is a recursive method based on the TDO at the last moment. The linear approximation model considers the MEMS accelerometer’s temperature and TDO as input and output, respectively. Next, the TDO is analyzed and optimized by the real-time compensation model and the three algorithms mentioned before. Moreover, the TDO is also compensated by the linear approximation model and PLA method as a comparison. The compensation results show that the three neural network methods and the PLA method effectively compensate for the temperature drift of the MEMS accelerometer, and the DLSTM + ISSA method achieves the best compensation effect. After compensation by DLSTM + ISSA, the three Allen variance coefficients of the MEMS accelerometer that bias instability, rate random walk, and rate ramp are improved from 5.43×10−4mg, 4.33×10−5mg/s12, 1.18×10−6mg/s to 2.77×10−5mg, 1.14×10−6mg/s12, 2.63×10−8mg/s, respectively, with an increase of 96.68% on average.

Enhancing the Accuracy of Low-Cost Inclinometers with Artificial Intelligence

“…The resolution of this device (0.00027°) is 7.4 times better than that reported by LARA (0.002°). Nevertheless, it is important to emphasize that achieving this level of accuracy in the commercial inclinometer comes at a considerably higher cost, EUR 15,000, which is 37.5 times higher than the price of LARA, at EUR 400 [49].…”

“…For instance, Podder [48] provides a literature review of AI applications for MEMS accelerometers, primarily focusing on fault detection and sensor diagnosis. Other works, such as Gou et al [49], Qi [50], Pan [51], and Wang [52], employed AI to mitigate temperature-related issues in low-cost MEMS accelerometers.…”

Reliability of MEMS inertial devices in mechanical and thermal environments: A review

“… 2021 [ 84 ] Enhancing the thermal stability of quality factor based on Joule effect in situ dynamic tuning by unitizing an active control loop Zero bias thermal drift improved by more than three times in the 100 °C range 2023 [ 85 ] MEMS accelerometer Temperature drift compensation algorithm based on deep long short-term memory (DLSTM) and improved sparrow search algorithm (ISSA) the three Allen variance coefficients of the MEMS accelerometer that bias instability, rate random walk, and rate ramp are improved from 5.43 × 10 −4 mg,4.33 × 10 −5 mg/s 1/2 , 1.18 × 10 −6 mg/s to 2.77 × 10 −5 mg, 1.14 × 10 −6 mg/s 1/2 , 2.63 × 10 −8 mg/s, respectively. 2023 [ 86 ] A reliable dual axis capacitive MEMS accelerometer Detecting the acceleration in the range of ± 50 g with a capacitance sensitivity of 38 fF/g in the temperature range of -40 °C to 100 °C 2020 [ 81 ] …”

Analysis of the Thermally Induced Packaging Effects on the Frequency Drift of Micro-Electromechanical System Resonant Accelerometer

“…Therefore, how to reduce the thermally induced frequency drift is the crucial problem to be solved urgently. Many efforts have been focused on significantly enhancing the scale factor and zero-bias stability of MEMS resonant accelerometers, such as novel temperature-insensitive device structure design [ 11 , 12 ]; stress isolation, with which to minimize the thermo-mechanical stress transmitted into the device [ 13 ]; operating temperature controls for the micro-oven integrated in the device [ 14 ]; and electronic temperature compensation of the output signal, either through the hardware or software system [ 15 , 16 ]. In addition, packaging material selection and structure optimization are also effective ways to radically reduce the thermal effects on MEMS resonant accelerometers [ 17 , 18 , 19 ], which, meanwhile, do not increase the power consumption or complexity of the system.…”