Characteristic Recognition of IC Chip’s Micro-Topography Defects Based on Image Projection Transformation and Energy Optimization Modeling


Article Preview

For accurate inspecting the micro-topography defects of IC chip, characteristic recognizing method of topography defects based on image projection transformation and energy optimization modeling is presented. First the topography conjugate images have been obtained by high-definition CCD cameras from different space positions, and then we extracting the relative positions of several selected IC defect characteristic-points, through establishing the relationship of image projecting transformation these point’s absolute three-dimensional coordinates can be calculated and determined, with energy optimization modeling the controlling curves are fitted, and then the defect topography in each enclosing spatial region which constituted by these controlling curves is structured. Mathematical characteristics are established according to the topography model, after training and adjusting BP network repeatedly the topography defects can be characteristic-recognized, and the recognizing results are gotten. In this experiment the performance comparison and analysis are implemented in several typical recognizing methods of image characteristic, which prove this new method’s accuracy and reliability and provide new idea for following IC chip’s precise detection.



Advanced Materials Research (Volumes 139-141)

Edited by:

Liangchi Zhang, Chunliang Zhang and Tielin Shi




Z. W. Liang et al., "Characteristic Recognition of IC Chip’s Micro-Topography Defects Based on Image Projection Transformation and Energy Optimization Modeling", Advanced Materials Research, Vols. 139-141, pp. 1990-1995, 2010

Online since:

October 2010




[1] S.L. ZHU, Taleb-Ahmed A. Pattern Recognition Letters, Vol. 27 (2006) No. 16: p.1893-(1904).

[2] LIAO S.Z. J. of Image and Graphics, Vol. 24 (2006). No. 7: pp.593-596.

[3] LI G. P, etal. Image and Vision Compu. Vol. 3 (2007) No. 1: pp.11-17.

[4] LI De-hua, etal. Computer and Digital Engineering. Vol. 23 (2004) No. 6: pp.37-40 (in Chinese).

[5] ZHU Hong-de, LOU Xiao-ping, DENG Wen-yi, LV Nai-guang. Computer Eng. and Appl., Vol. 11 23 (2006) No. 3: pp.217-218.

[6] WANG Yue-zong, etal. Computer Engineering and Applications, Vol. 25 (2003) No. 9: p.98100.

[7] Zhu H. Foundation of Digital Image Processing [M]. ( Press of Science. China, 2005).

[8] Zheng N N. Computer Vision and Pattern Recognition [M]. ( Press of National Defense Industry. China, 2005) (in Chinese).

[9] Jia Y D. Machine Vision [M]. ( Press of Science. China 2006).

[10] Cheng Z X. Wavelet Analyze Algorithm and Application [M]. (Press of Xi'an Jiaotong University. China 2005).

[11] H Zhu, J Zhou, H Li. SPIE. 2007. 4875: pp.651-655.

[12] Zhao Z Q, Xiong Y J. Computer of Industry Controlling. Vol. 18. 2005, No. 10: pp.1-2.

[13] Yuan H. Journal of S.C.U. T, Vol. 30. 2002. No. 4: pp.60-64.

[14] Zheng Lian. Journal of Beijing Institute of Technology, Vol. 17. 1997, No. 8: pp.193-198.

[15] Shi Min, Xie Sheng-li. Journal of S.C.U. T, Vol. 34. 2006, No. 1: pp.18-23.

[16] Song C. L, etal. J. of China Univ. of Mining & Technology, Vol. 17. 2007. No. 3: pp.121-125.

Fetching data from Crossref.
This may take some time to load.