International audienceIn the current work, we consider the inverse problem in scatterometry which consists in determining the feature shape from an experimental ellipsometric signature. The reformulation of the given nonlinear identification problem was considered as a parametric optimization problem using the Least Square criterion. In this work, a design procedure for global robust optimization is developed using Kriging and global optimization approaches. Robustness is determined by Kriging model to reduce the number of real functional calculations of Least Square criterion. The technical of the global optimization methods is adopted to determine the global robust optimum of a surrogate model
