Temperature compensation and selection optimization of MEMS pressure sensor based on PSO-RBF

MEMS pressure sensors are pressure measuring devices manufactured based on MEMS technology, widely used in industries such as automotive, consumer electronics, and healthcare. Compared with traditional pressure sensors, MEMS pressure sensors have advantages such as miniaturization, high precision, low power consumption, and low cost. However, its output value is greatly affected by temperature, which limits its further application in industrial production. In this study, we perform temperature compensation on Shuangqiao CYG1100 series silicon piezoresistive MEMS pressure sensor using the PSO-RBF neural network. At the same time, the temperature compensation points of the MEMS pressure sensor are optimized, reducing the selection of temperature points while ensuring a certain output accuracy, and improving the efficiency of temperature compensation of the MEMS pressure sensor. This has important significance in the testing field of MEMS pressure sensors.