Identification and reconstruction of diffraction structures in optical scatterometry using support vector machine method

A library search is a widely used method for the reconstruction of diffraction structures in optical scatterometry. In a library search, if the actual geometrical model of a measured signature is different from the model used in the establishment of a library, the search result will be meaningless. Therefore, the identification of the geometrical profile for a measured signature is critical. In addition, fast searching of the library is essential to find a best-matched signature even though the library may have huge amounts of data. The authors propose a support vector machine (SVM)-based method to deal with these issues. First, an SVM classifier is trained to identify the geometrical profile of a diffraction structure from its measured signature, and then another set of several SVM classifiers are trained to map the measured signature into a sublibrary to accelerate the search process. Simulations and experiments have demonstrated that the SVM classifier can identify the geometrical profile of one-dimensional trapezoidal gratings accurately, and the SVM-based library search strategy can achieve a fast and robust extraction of parameters for diffraction structures.