微机电系统麦克风灵敏度不确定性分析

由于制造工艺造成振膜参数偏差导致了微机电系统麦克风灵敏度的不确定性,为了减少计算时间, 提高仿真效率,提出基于人工神经网络的拉丁超立方蒙特卡罗模拟,以分析多晶硅圆形固支振膜麦克风灵敏度的不确定性。实验结果表明, 麦克风灵敏度的合格率为92.9%,仿真耗时小于10 s 。相比传统随机采样蒙特卡罗模拟,相同仿真精度拉丁超立方采样数仅为传统采样的11%;研究了振膜参数对麦克风灵敏度概率分布的影响。采用正态分布拟合仿真结果,得到灵敏度分布的均值与标准差。计算结果表明,振膜半径对灵敏度分布的影响最为明显,厚度的影响次之,弹性模量只影响分布的均值,不影响标准差。拉丁超立方蒙特卡罗模拟是一种分析麦克风灵敏度不确定性的高精度、高效率方法。

Analysis of sensitivity uncertainty of the MEMS microphone based on Latin hypercube Monte Carlo simulation

Due to the diaphragm uncertainties from manufacturing processes, the MEMS microphone may exhibit significant variations in their performance compared to the nominal design. In order to reduce calculation time and improve simulation efficiency, this paper presents the Latin hypercube Monte Carlo Simulation (LHMCS) based on artificial neural networks (ANNs), which is used to analyze the sensitivity uncertainty of the polysilicon circular clamped diaphragm microphone. Experimental results show that the simulated qualified rate of the MEMS microphone is 92.9% and the time-consuming is less than 10 seconds and that compared with the traditional Monte Carlo simulation based on random sampling, LHMCS needs only 11% of the sampling number at the same accuracy. This paper also analyzes the effects of nominal design values of the diaphragm on the probability distributions of microphone sensitivities. The mean values and standard deviations of the sensitivity distributions are obtained by fitting the simulation results with normal distribution. The results show that three main factors affecting the distribution in the order from strength to weakness are radius, thickness, and young modulus. Young modulus only affects the mean value, but does not affect the standard deviation. The presented LHMCS with high accuracy and efficiency is an alternative to the traditional methods.