In this work, theoretically/mathematically simulated (TMS) model is presented for the photoacoustic (PA) frequency response of a semiconductor in a minimum volume PA cell. By analyzing of the TMS model, the influences of thermal diffusivity and linear coefficient of thermal expansion on silicon sample PA frequency response were investigated and two methods were developed for their estimation. The first one is a self consistent inverse procedure for solving the exponential problems of mathematical physics, based on regression. The second one, a well trained three-layer perceptron with back propagation, based upon theory of artificial neural networks, is developed and presented. These two inverse problem solving concepts are applied to thermo-elastic characterization of silicon, compared and discussed in the domain of semiconductor characterization.

在这项工作中，提出了最小体积 PA 单元中半导体的光声 (PA) 频率响应的理论/数学模拟 (TMS) 模型。通过对 TMS 模型的分析，研究了热扩散率和线性热膨胀系数对硅样品 PA 频率响应的影响，并开发了两种估计方法。第一个是基于回归的自洽逆过程，用于解决数学物理的指数问题。第二个是基于人工神经网络理论的训练有素的具有反向传播的三层感知器。这两个逆问题求解概念应用于硅的热弹性表征，在半导体表征领域进行比较和讨论。