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.
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