Photoacoustic characterization of TiO2 thin-films deposited on Silicon substrate using neural networks

TL;DR

This study employs neural networks to accurately determine the thermal, elastic, and geometric properties of a TiO2 thin film on silicon, demonstrating improved multi-parameter prediction over single-parameter models.

Contribution

The paper introduces a neural network approach for simultaneous prediction of multiple thin-film parameters, enhancing accuracy compared to single-parameter models.

Findings

Multi-parameter neural network achieved highest accuracy.

Single-parameter neural networks were less reliable.

Neural networks effectively characterized thin-film properties.

Abstract

In this paper, the possibility of determining the thermal, elastic and geometric characteristics of a thin TiO2 film deposited on a silicon substrate, thickness 30 mikrons, in the frequency range of 20 to 20 kHz with neural networks was analyzed. For this purpose, the substrate parameters remained the known and constant in the two-layer model and nano layer thin-film parameters were changed: thickness, expansion and thermal diffusivity. Prediction of these three parameters was analyzed separately with three neural networks and all of these together by fourth neural network. It was shown that neural network, which analyzed all three parameters at the same time, achieved the highest accuracy, so the use of networks that provide predictions for only one parameter is less reliable.