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All indirect optical metrology techniques, such as spectroscopic ellipsometry, reflectometry or scatterometry, for characterization of surfaces, thin films and complex 2D/3D multilayer structures require an appropriate modeling. Parametric sensitivity analysis (SA) is as an essential prerequisite step in optical metrology data modeling to quantify the relative importance of optical model parameters and to identify those with little influence in order to simplify a model. In our previous studies, we have detailed the use of the Morris or Elementary Effect (EE) method, a screening type SA procedure, applied it to the spectroscopic ellipsometry data processing and investigated different types of its convergence. The present study is a continuation of these investigations, extending the application of the EE method for ellipsometric modeling. The method is a global SA technique and uses a stepping of m parameters along certain so-called “trajectories”, or sequences of points in parameter space, randomly constructed in order to maximally fill the volume of the m-dimensional parameter space. However, it is reputed that the EE method relies heavily on a sampling strategy, or a way of selecting “optimized trajectories” in parameter space, i.e., the selection of a necessary number of trajectories chosen to be well spread over the space to properly cover the entire realistic ranges of all input factors. Here, we test some sampling methods for selecting trajectories with possibly different distributions and investigate their effects on the estimation of various sensitivity measures in spectroscopic ellipsometry data modeling. The results indicate that the performance of the sampling strategy should not be judged only by maximization of the trajectory spread but also by certain convergence criteria for the sensitivity index μ*.
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