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Proceedings Paper

Large-scale absolute surface reconstruction
Author(s): Gao-feng Wu; Hai-yang Quan; Wei-hong Song; Yong-qian Wu; Fan Wu
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Paper Abstract

In order to realize large-scale absolute surface reconstruction, a generalized iterative optimization method for solving the three-flat problem is studied. First, the idea of model-based absolute surface reconstruction is proposed, which considers the problems of absolute surface reconstruction as inverse problems. Then we take the three-flat problems as an example, we introduced two generalized iterative optimization methods for three-flat model. Finally, by both simulation and experiment, it is concluded that the block SOR method with an optimal relaxation factor converges much faster and saves more computational costs and memory space without reducing accuracy. Both simulation and experimental results indicate that the proposed iterative optimization methods are effective for solving the three-flat problem with pixel-level spatial resolution and the measuring precision of two separate measurements is 0.6 nm rms, and the cross-check test result is 0.8 nm rms. It is concluded that the proposed method can correctly reconstruct absolute figures with high efficiency and pixel-level spatial resolution.

Paper Details

Date Published: 27 September 2016
PDF: 7 pages
Proc. SPIE 9684, 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment, 96843L (27 September 2016); doi: 10.1117/12.2243603
Show Author Affiliations
Gao-feng Wu, Institute of Optics and Electronics (China)
Univ. of Chinese Academy of Sciences (China)
Hai-yang Quan, Institute of Optics and Electronics (China)
Univ. of Chinese Academy of Sciences (China)
Wei-hong Song, Institute of Optics and Electronics (China)
Yong-qian Wu, Institute of Optics and Electronics (China)
Fan Wu, Institute of Optics and Electronics (China)


Published in SPIE Proceedings Vol. 9684:
8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test, Measurement Technology, and Equipment
Yudong Zhang; Fan Wu; Ming Xu; Sandy To, Editor(s)

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