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17 January 2013 Identification and reconstruction of diffraction structures in optical scatterometry using support vector machine method
Jinlong Zhu, Shiyuan Liu, Chuanwei Zhang, Xiuguo Chen, Zhengqiong Dong
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Funded by: National Natural Science Foundation of China, National Instrument Development Specific Project of China
Journal of Micro/Nanolithography, MEMS, and MOEMS, Vol. 12, Issue 1, 013004 (January 2013). https://doi.org/10.1117/1.JMM.12.1.013004
Abstract
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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Jinlong Zhu, Shiyuan Liu, Chuanwei Zhang, Xiuguo Chen, and Zhengqiong Dong "Identification and reconstruction of diffraction structures in optical scatterometry using support vector machine method," Journal of Micro/Nanolithography, MEMS, and MOEMS 12(1), 013004 (17 January 2013). https://doi.org/10.1117/1.JMM.12.1.013004
Published: 17 January 2013
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Cited by 19 scholarly publications.
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KEYWORDS
Diffraction
Scatterometry
Error analysis
Computer simulations
Inverse optics
Algorithm development
Geometrical optics
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