Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks

In this paper, a simple multilayer perceptron neural network with forward signal propagation was designed and used to simultaneously determine the main physical parameters, such as: the thermal diffusivity, thermal expansion coefficient and thickness, from the transmission, frequency-modulated photoacoustic response of the sample. The amplitude and phase responses of the transmission open-cell photoacoustic signals were calculated in n-type silicon plates using a theoretical model and were used to train and test a neural network. The simulation was done in the modulation frequency range from 20 Hz to 20 kHz and using a wide range of expected values of thermal diffusivity and the thermal coefficient of expansion for semiconductor samples as well as their thickness. The advantages and disadvantages of neural networks utilization as an appropriate mathematical tool designated for semiconductor measurement-oriented purposes are analyzed. Network reliability, precision, and the possibility of operation in real time have been verified on an independent set of signals, establishing photoacoustics as a competitive and powerful technique assigned for material characterization.