Automated discrimination between digs and dust particles on optical surfaces with dark-field scattering microscopy

Applied Optics

Volumn , Issue 23, 2014, Pages 5131-5140

  Li, Lu ^a   Liu, Dong ^a   Cao, Pin ^a   Xie, Shibin ^a,b   Li, Yang ^a,b   Chen, Yangjie ^b   Yang, Yongying ^a  

^a ZHEJIANG UNIVERSITY   (China)

^b Hangzhou Diwei Electro hydraulic NUMERICAL Control Technology Co   (China)

Author keywords

[No Author keywords available]

Indexed keywords

MORPHOLOGY; PATTERN RECOGNITION; SUPPORT VECTOR MACHINES; SURFACE DEFECTS; TEXTURES;

CLASSIFICATION MODELS; CLASSIFICATION RESULTS; DARK-FIELD SCATTERINGS; DISCRIMINATION RATES; DUST PARTICLE; MORPHOLOGY ANALYSIS; OPTICAL SURFACES; TRAINING SETS;

DUST;

EID: 84942372863     PISSN: 1559128X     EISSN: 21553165     Source Type: Journal    
DOI: 10.1364/AO.53.005131     Document Type: Article

References (36)

1. L. R. Baker, Metrics for High-Quality Specular Surfaces (SPIE, 2004).
2. D. Aikens, "Objective measurement of scratch and dig, " in Optical Fabrication and Testing, OSA Technical Digest (Optical Society of America, 2012), paper OTu2D.5.
3. F. Rainer, R. K. Dickson, R. T. Jennings, J. F. Kimmons, S. M. Maricle, R. P. Mouser, S. Schwartz, and C. L. Weinzapfel, "Development of practical damage mapping and inspection systems, " Proc. SPIE 3492, 556-563 (1999).
4. M. D. Feit and A. M. Rubenchik, "Influence of subsurface cracks on laser-induced surface damage, " Proc. SPIE 5273, 264-272 (2004).
5. B. Ma, Y. Zhang, H. Ma, H. Jiao, X. Cheng, and Z. Wang, "Influence of incidence angle and polarization state on the damage site characteristics of fused silica, " Appl. Opt. 53, A96-A102 (2014).
6. F. Y. G. E. Nin, A. Salleo, T. V. Pistor, and L. L. Chase, "Role of light intensification by cracks in optical breakdown on surfaces, " J. Opt. Soc. Am. A 18, 2607-2616 (2001).
7. L. M. Kegelmeyer, R. Clark, R. R. Leach, Jr., D. McGuigan, V. M. Kamm, D. Potter, J. T. Salmon, J. Senecal, A. Conder, M. Nostrand, and P. K. Whitman, "Automated optics inspection analysis for NIF, " Fusion Eng. Des. 87, 2120-2124 (2012).
8. S. Demos, M. Staggs, K. Minoshima, and J. Fujimoto, "Characterization of laser induced damage sites in optical components, " Opt. Express 10, 1444-1450 (2002).
9. P. K. Sinha, Image Acquisition and Preprocessing for Machine Vision Systems (SPIE, 2012).
10. D. Liu, Y. Yang, L. Wang, Y. Zhuo, C. Lu, L. Yang, and R. Li, "Microscopic scattering imaging measurement and digital evaluation system of defects for fine optical surface, " Opt. Commun. 278, 240-246 (2007).
11. Y. Yang, S. Wang, X. Chen, L. Li, P. Cao, L. Yan, Z. Cheng, and D. Liu, "Sparse microdefect evaluation system for large fine optical surfaces based on dark-field microscopic scattering imaging, " Proc. SPIE 8838, 883806 (2013).
12. X. Gao, Y. Yang, P. Zhao, and B. Xiao, "Super-smooth surface defects measurement and evaluation system, " Proc. SPIE 7656, 76560A (2010).
13. D. Liu, S. Wang, P. Cao, L. Li, Z. Cheng, X. Gao, and Y. Yang, "Dark-field microscopic image stitching method for surface defects evaluation of large fine optics, " Opt. Express 21, 5974-5987 (2013).
14. X. Chen, D. Liu, S. Wang, P. Cao, X. Gao, and Y. Yang, "Research on digital calibration method for optical surface defect dimension, " Proc. SPIE 8417, 84173L (2012).
15. P. Cao, D. Liu, P. Zhao, Y. Yang, and S. Wang, "Application of image entropy evaluation function for the leveling of large aperture components in auto defects detecting, " Proc. SPIE 8417, 84173K (2012).
16. Y. Liao, X. Weng, C. W. Swonger, and J. Ni, "Defect detection and classification of machined surfaces under multiple illuminant directions, " Proc. SPIE 7798, 77981T (2010).
17. Ultrapointe Corporation, "Method for characterizing defects on semiconductor wafers, " U.S. patent 6, 288, 782 B1 (11 September 2001).
18. ADE Corporation, "Method for discriminating between holes in and particles on a film covering a substrate, " U.S. patent 6,486,946 B1 (26 November 2002).
19. A. Doicu, Y. Eremin, and T. Wriedt, "Scattering of evanescent waves by a particle on or near a plane surface, " Comput. Phys. Commun. 134, 1-10 (2001).
20. L.-P. Zhao, X. Li, and Z.-P. Fang, "TIR illumination technology for defect inspection of plastic ophthalmic lenses, " Opt. Precis. Eng. 19, 2247-2254 (2011).
21. W. J. Choi, S. Y. Ryu, J. K. Kim, J. Y. Kim, D. U. Kim, and K. S. Chang, "Fast mapping of absorbing defects in optical materials by full-field photothermal reflectance microscopy, " Opt. Lett. 38, 4907-4910 (2013).
22. A. K. Jain, R. P. W. Duin, and M. Jianchang, "Statistical pattern recognition: a review, " IEEE Trans. Pattern Anal. Mach. Intell. 22, 4-37 (2000).
23. M. Kamruzzaman, G. ElMasry, D. Sun, and P. Allen, "Application of NIR hyperspectral imaging for discrimination of lamb muscles, " J. Food Eng. 104, 332-340 (2011).
24. J. Zhao, Q. Chen, J. Cai, and Q. Ouyang, "Automated tea quality classification by hyperspectral imaging, " Appl. Opt. 48, 3557-3564 (2009).
25. M. S. A. Marcos, M. Soriano, and C. Saloma, "Classification of coral reef images from underwater video using neural networks, " Opt. Express 13, 8766-8771 (2005).
26. L. M. Kegelmeyer, "Applying avatar machine learning to NIF optics inspection analysis, " in NASA Conference on Intelligent Data Understanding, 6 October 2010, p. 42, https://c3.ndc.nasa.gov/dashlink/resources/221.
27. G. M. Abdulla, L. M. Kegelmeyer, Z. M. Liao, and W. Carr, "Effective and efficient optics inspection approach using machine learning algorithms, " Proc. SPIE 7842, 78421D (2010).
28. S. Wang, D. Liu, Y. Yang, X. Chen, P. Cao, L. Li, L. Yan, Z. Cheng, and Y. Shen, "Distortion correction in surface defects evaluating system of large fine optics, " Opt. Commun. 312, 110-116 (2014).
29. H. Ng, "Automatic thresholding for defect detection, " Pattern Recogn. Lett. 27, 1644-1649 (2006).
30. J. E. Jackson, A User's Guide to Principal Components (Wiley, 2005).
31. I. Jolliffe, Principal Component Analysis (Wiley, 2005).
32. S. Theodoridis and K. Koutroumbas, Pattern Recognition (Academic, 2008).
33. C. C. Burges, "A tutorial on support vector machines for pattern recognition, " Data Min. Knowl. Disc. 2, 121-167 (1998).
34. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, "Choosing multiple parameters for support vector machines, " Mach. Learn. 46, 131-159 (2002).
35. V. Vapnik, The Nature of Statistical Learning Theory (Springer, 2000).
36. http://www.esat.kuleuven.ac.be/sista/lssvmlab/.