3 April 2012 Classification and recognition of diffraction structures using support vector machine in optical scatterometry
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The library search is a widely used method for reconstruction of diffraction structures in optical scatterometry. In library search, an optimized set of geometrical parameters for a diffraction structure can be achieved by searching for a best match between the measured signatures and the simulated ones. The search speed and accuracy is the key to guarantee the effectiveness of this method, and some a priori geometrical model is necessary. Once the actual geometrical model of a measured signature is different from the model used in the establishment of library, the search result will be meaningless. Therefore, the classification and recognition of the geometrical profile for a measured signature is critical. In this paper, we develop two support vector machine (SVM) classifiers to deal with issue. One classifier is used to identify the geometrical profile of a diffraction structure from its measured signature, and the other one is to map the whole search range of the identified diffraction structure into a smaller one. By using some reliable and mature search algorithms, we can fast and accurately reconstruct the geometry profile of a diffraction structure in this optimized small range. Simulation and experiment have demonstrated that the SVM classifiers can identify the geometrical profile of one-dimensional trapezoidal gratings accurately, and the SVM-based library search strategy can achieve a fast and accurate extraction of parameters for diffraction structures.
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Jinlong Zhu, Jinlong Zhu, Shiyuan Liu, Shiyuan Liu, Chuanwei Zhang, Chuanwei Zhang, Xiuguo Chen, Xiuguo Chen, Zhengqiong Dong, Zhengqiong Dong, "Classification and recognition of diffraction structures using support vector machine in optical scatterometry", Proc. SPIE 8324, Metrology, Inspection, and Process Control for Microlithography XXVI, 83242S (3 April 2012); doi: 10.1117/12.916259; https://doi.org/10.1117/12.916259

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