  Nonlinear regression methods, such as local optimization algorithms, are
widely used in the extraction of nanostructure profile parameters in optical
scatterometry. The success of local optimization algorithms heavily relies on
the estimated initial solution. If the initial solution is not appropriately
selected, it will either take a long time to converge to the global solution or
will result in a local one. Thus, it is of great importance to developing a
method to guarantee the capture of a globally optimal solution. In this paper,
we propose a method that combines the support vector machine and
Levenberg-Marquardt algorithm for the fast and accurate parameters extraction.
The SVM technique is introduced to pick out a sub-range in the rough ranges of
parameters, in which an arbitrary selected initial solution for the LM
algorithm is then able to achieve the global solution with a higher
possibility. Simulations and experiments conducted on a one-dimensional Si
grating and a deep-etched multilayer grating have demonstrated the feasibility
and efficiency of the proposed method.
