Demonstration of the feasibility of a complete ellipsometric characterization method based on an artificial neural network

Abstract

Ellipsometry is an optical technique that is widely used for determining optical and geometrical properties of optical thin films. These properties are in general extracted from the ellipsometric measurement by solving an inverse problem. Classical methods like the Levenberg-Marquardt algorithm are generally too long, depending on direct calculation and are very sensitive to local minima. In this way, the neural network has proved to be an efficient tool for solving these kinds of problems in a very short time. Indeed, it is rapid and less sensitive to local minima than the classical inversion method. We suggest a complete neural ellipsometric characterization method for determining the index dispersion law and the thickness of a simple SiO(2) or photoresist thin layer on Si, SiO(2), and BK7 substrates. The influence of the training couples on the artificial neural network performance is also discussed.