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Proceedings Paper

Optimal architecture of a neural network for a high precision in ellipsometric scatterometry
Author(s): Issam Gereige; Stéphane Robert; Gérard Granet
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Paper 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.

Paper Details

Date Published: 10 September 2007
PDF: 11 pages
Proc. SPIE 6648, Instrumentation, Metrology, and Standards for Nanomanufacturing, 66480G (10 September 2007); doi: 10.1117/12.734278
Show Author Affiliations
Issam Gereige, Univ. Jean Monnet (France)
Stéphane Robert, Univ. Jean Monnet (France)
Gérard Granet, Lab. des Sciences et Matériaux pour l'Electronique et d'Automatique, CNRS (France)


Published in SPIE Proceedings Vol. 6648:
Instrumentation, Metrology, and Standards for Nanomanufacturing
Michael T. Postek; John A. Allgair, Editor(s)

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