Optimal architecture of a neural network for a high precision in ellipsometric scatterometry

Issam Gereige; Stéphane Robert; Gérard Granet

doi:10.1117/12.734278

10 September 2007 Optimal architecture of a neural network for a high precision in ellipsometric scatterometry

Issam Gereige, Stéphane Robert, Gérard Granet

Proceedings Volume 6648, Instrumentation, Metrology, and Standards for Nanomanufacturing; 66480G (2007) https://doi.org/10.1117/12.734278
Event: NanoScience + Engineering, 2007, San Diego, California, United States

Abstract

Neural networks (NN) have received a great deal of interest over the last few years. They are being applied accross a wide range of problems in pattern recognition, artificial intelligence, and classification as well as in the inverse problem of scatterometry. Optical scatterometry is a non-direct characterization method that has been widely employed in the semiconductor industry for critical dimensions control. It is based on the analysis of the light scattered from periodic structures. This analysis consists of the resolution of an inverse problem in order to determine the parameters defining the geometrical shape of the structure. In this work, we will study the performances of the NN according to various internal parameters when it is applied to solve the scattered problem. This will allow us to examine how a NN reacts and to select the optimal configuration of these parameters leading to a rapid and accurate characterization.

Citation Download Citation

Issam Gereige, Stéphane Robert, and Gérard Granet "Optimal architecture of a neural network for a high precision in ellipsometric scatterometry", Proc. SPIE 6648, Instrumentation, Metrology, and Standards for Nanomanufacturing, 66480G (10 September 2007); https://doi.org/10.1117/12.734278

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