微机械谐振器温度特性分析及频率漂移补偿  

Temperature characteristics analysis and frequency drift compensation for MEMS resonators

作　　者：刘恒[1] 郭月圆 张玉 LIU Heng;GUO Yueyuan;ZHANG Yu(School of Electronic&Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学电子与信息工程学院,南京210044

出　　处：《中国惯性技术学报》2023年第3期275-281,291,共8页Journal of Chinese Inertial Technology

基　　金：国家重点研发计划(2019YFC1804704)。

摘　　要：针对微机械谐振器工作温度的变化导致谐振频率漂移问题,提出了基于RBF神经网络的频率漂移补偿方法。首先理论分析了微机械谐振器的温度特性:在10~90℃范围内,计算得到杨氏模量随温度变化而导致的频率漂移为-1.325Hz/℃,有限元仿真漂移为-1.353Hz/℃。其次,以绝对温度、温度变化量、测试频率作为RBF神经网络的输入,恒温箱测试频率为目标值,利用某空间实验仓的样本训练构建了RBF神经网络模型。最后在10~90℃范围内,进行了频率漂移补偿验证,实验结果表明,0.5℃间隔下升温补偿相对误差为7.1 ppm,降温补偿相对误差为4.5 ppm,较“GA+BP”补偿方法,相对误差减少50%以上。证明了所提补偿方法具有较好的适用性。In order to solve the problem of resonant frequency drift caused by changes in the operating temperature of micromechanical resonators,a frequency drift compensation method based on RBF neural network is proposed.Firstly,the temperature characteristics of the micromechanical resonator are analyzed theoretically.In the range of 10 to 90℃,the frequency drift caused by changes in Young's modulus with temperature is calculated to be-1.325Hz/℃,the finite element simulation drift is-1.353Hz/℃.Secondly,taking the absolute temperature,temperature change and test frequency as the input of RBF neural network and the test frequency of thermostat as the target value,the RBF neural network model is constructed by using the sample training in the space experiment chamber.Finally,in the range of 10~90℃,the frequency drift compensation verification is carried out.The experimental results show that the relative error of temperature rise compensation is 7.1 ppm and that of temperature drop compensation is 4.5 ppm at 0.5℃interval,which is reduced by more than 50%than that of"GA+BP"compensation method.It is proved that the proposed compensation method has good applicability.

关 键 词：微机械谐振器 温度特性 频率补偿 神经网络 

分 类 号：U666.1[交通运输工程—船舶及航道工程]