Overview of Research Methods for Temperature Error Compensation of Sensors

Yonglei Shi; Erqian Ma

doi:10.54691/hr73ss95

Authors

Yonglei Shi
Erqian Ma

DOI:

https://doi.org/10.54691/hr73ss95

Keywords:

Temperature; Error; Compensation; Sensor; Accuracy.

Abstract

Sensors are widely used in various industrial and agricultural production practices. However, due to factors such as materials and structures used in manufacturing or packaging, their output signals are easily affected by changes in environmental temperature, leading to measurement errors. Sensor temperature error compensation technology is an important means to improve measurement accuracy, which is of great significance for sensors to play a greater role in industrial production. By reviewing relevant literature in recent years, this article summarizes the commonly used hardware compensation methods for sensor error compensation, methods for controlling the working temperature of sensors, methods for optimizing peripheral circuit compensation, and software compensation. With the continuous development of sensor technology, temperature error compensation methods are also constantly innovating and improving. In the future, temperature error compensation for sensors will pay more attention to real-time, adaptive, and intelligent capabilities to meet high-precision measurement requirements in various complex environments. This article helps researchers to gain a deeper understanding of the research methods and current status of sensor temperature error compensation, and also provides some research ideas for researchers in related fields.

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References

[1] Wu L, Zhao G, Yin J, et al. A thermal drift compensation method for precision sensors considering historical temperature state[J]. IEEE Transactions on Industrial Electronics, 2021, 68(12): 12821-12829.

[2] Cao Y, Xu W, Lin B, et al. A method for temperature error compensation in fiber-optic gyroscope based on machine learning[J]. Optik, 2022, 256: 168765.

[3] He J, Xie J, He X, et al. Analytical study and compensation for temperature drifts of a bulk silicon MEMS capacitive accelerometer[J]. Sensors and Actuators A: Physical, 2016, 239: 174-184.

[4] Yin Y, Fang Z, Liu Y, et al. Temperature-insensitive structure design of micromachined resonant accelerometers[J]. Sensors, 2019, 19(7): 1544.

[5] Chen Z, Guo M, Zhang R, et al. Measurement and isolation of thermal stress in silicon-on-glass MEMS structures[J]. Sensors, 2018, 18(8): 2603.

[6] Hao Y, Yuan W, Xie J, et al. Design and verification of a structure for isolating packaging stress in SOI MEMS devices[J]. IEEE Sensors Journal, 2016, 17(5): 1246-1254.

[7] Cui J, Yang H, Li D, et al. A silicon resonant accelerometer embedded in an isolation frame with stress relief anchor[J]. Micromachines, 2019, 10(9): 571.

[8] Zotov S A, Simon B R, Trusov A A, et al. High quality factor resonant MEMS accelerometer with continuous thermal compensation[J]. IEEE Sensors Journal, 2015, 15(9): 5045-5052.

[9] Yen T H, Tsai M H, Chang C I, et al. Improvement of CMOS-MEMS accelerometer using the symmetric layers stacking design[C]//SENSORS, 2011 IEEE. IEEE, 2011: 145-148.

[10] Yang D, Woo J K, Lee S, et al. A micro oven-control system for inertial sensors[J]. Journal of Microelectromechanical Systems, 2017, 26(3): 507-518.

[11] Shin D D, Chen Y, Flader I B, et al. Epitaxially encapsulated resonant accelerometer with an on-chip micro-oven[C]//2017 19th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS). IEEE, 2017: 595-598.

[12] Lee K I, Takao H, Sawada K, et al. A three-axis accelerometer for high temperatures with low temperature dependence using a constant temperature control of SOI piezoresistors[C]//The Sixteenth Annual International Conference on Micro Electro Mechanical Systems, 2003. MEMS-03 Kyoto. IEEE. IEEE, 2003: 478-481.

[13] Guo runqiu, Zheng Xiaodong, Wang Cheng. Research on Identification of Static Temperature Model and Temperature Compensation Method for Accelerometers [J]. Journal of Xi'an University of Electronic Science and Technology, 2007 (03): 438-442

[14] Lakdawala H, Fedder G K. Temperature stabilization of CMOS capacitive accelerometers[J]. Journal of Micromechanics and Microengineering, 2004, 14(4): 559.

[15] Sun H, Jia K, Liu X, et al. A CMOS-MEMS gyroscope interface circuit design with high gain and low temperature dependence[J]. IEEE Sensors Journal, 2011, 11(11): 2740-2748.

[16] Yin T, Wu H, Wu Q, et al. A TIA-based readout circuit with temperature compensation for MEMS capacitive gyroscope[C]//2011 6th IEEE International Conference on Nano/Micro Engineered and Molecular Systems. IEEE, 2011: 401-405.

[17] Liu Yidong, Liu Jie, et al. Temperature compensation method for micro mechanical accelerometers based on resonant frequency [J]. Journal of Tianjin University (Natural Science and Engineering Technology Edition), 2015, 48 (07): 658-662

[18] Gu Chunlei, Lu Jinggui, Wang Yizu, et al. Fiber optic gyroscope temperature compensation based on GA-BP neural network [J]. Instrument Technology and Sensors, 2018 (03): 113-116

[19] Wang Faliang, Xu Dacheng. Temperature compensation of MEMS accelerometer based on PSO-BP neural network [J]. Sensors and Microsystems, 2019,38 (02): 19-22

[20] Yang Zhimei, Zhou Xiaolong, Xu Dacheng. Research on Temperature Compensation Method for MEMS Accelerometers Based on LM-BP Neural Network Model [J]. Instrument Technology and Sensors, 2015 (11): 30-33

[21] Shiau, J. K, C. X. Huang, M. Y. Chang. Noise Characteristics of MEMS Gyro's Null Drift and Temperature Compensation[J].J APPL SCI ENG 2012, 15:239-246.

[22] Han Z, Hong L, Meng J, et al. Temperature drift modeling and compensation of capacitive accelerometer based on AGA-BP neural network[J]. Measurement, 2020, 164: 108019.

[23] Wang S, Zhu W, Shen Y, et al. Temperature compensation for MEMS resonant accelerometer based on genetic algorithm optimized backpropagation neural network[J]. Sensors and Actuators A: Physical, 2020, 316: 112393.

[24] Zhu M, Pang L, Xiao Z, et al. Temperature drift compensation for High-G MEMS accelerometer based on RBF NN improved method[J]. Applied Sciences, 2019, 9(4): 695.

[25] Xu Xiaoting, Shen Xiaolin. Temperature drift compensation of MEMS gyroscope based on RBF neural network [J]. Micro nano Electronics Technology, 2018, 55 (11): 819-823

[26] Chong S, Rui S, Jie L, et al. Temperature drift modeling of MEMS gyroscope based on genetic-Elman neural network[J]. Mechanical Systems and Signal Processing, 2016, 72: 897-905.

[27] Lijun Z, Fang M. Temperature Drift Compensation of MEMS Gyroscope Array Based on Elman Neural Networks[C]//2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2019: 1643-1646.

[28] Du Jin, Li Jie, Feng Kaiqiang, et al. Application of Polynomial Fitting in Zero Point Random Drift Suppression of MEMS Gyroscopes [J]. Journal of Sensing Technology, 2016,29 (05): 729-732

[29] Ali M. Compensation of temperature and acceleration effects on MEMS gyroscope[C]//2016 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2016: 274-279.

[30] Fontanella R, Accardo D, Moriello R S L, et al. MEMS gyros temperature calibration through artificial neural networks[J]. Sensors and Actuators A: Physical, 2018, 279: 553-565.

[31] Zhang Lijie, Chang Ji. A Temperature Model Identification and Temperature Compensation Method for MEMS Accelerometers [J]. Journal of Sensing Technology, 2011, 24 (11): 1551-1555.