基于PSO-RBF的MEMS压力传感器温度补偿及选点优化

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

  • 摘要: 微机电系统(MEMS)压力传感器是一种基于MEMS技术制造的压力测量器件,广泛应用于工业、汽车、消费电子、医疗等领域。与传统的压力传感器相比,MEMS压力传感器具备小型化、高精度、低功耗、成本低等优势。然而,其输出值受温度影响较大,这一问题限制了其在工业生产领域的进一步应用。本研究采用PSO-RBF神经网络对双桥CYG1100系列硅压阻MEMS压力传感器进行了温度补偿,同时对其温度补偿选点进行了优化。在保证一定输出精度的前提下,减少了温度点的选取,提升了MEMS压力传感器温度补偿的效率。本研究的结果,在MEMS压力传感器的测试领域中有着重要意义。

     

    Abstract: 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.

     

/

返回文章
返回