Automated vision positioning system for dicing semiconductor chips using improved template matching method

  • Fengjun ChenEmail author
  • Xiaoqi Ye
  • Shaohui Yin
  • Qingshan Ye
  • Shuai Huang
  • Qingchun Tang


This study proposes an automated vision positioning system to realize high-efficient and high-precision positioning and dicing of semiconductor chips in an automatic dicing saw. In this method, image pyramid construction was established to improve the searching speed of feature images by using the pyramid hierarchical search strategy. Hough transformation was used to obtain the approximate angle of the feature images of the semiconductor chips. The improved template matching approach based on the initial angle was proposed to rapidly calculate rotation angle and feature position. Polynomial fitting was adopted to achieve sub-pixel positioning accuracy. Experimental results showed that the proposed algorithm can realize high-precision and real-time recognition under the weak light, strong light, uneven illumination, and rotation angle. The success rate is 99.25%, and the time consumed is only 1/4 of the normalized cross-correlation algorithm. The vision positioning and dicing experiment of the semiconductor chips was carried out on a high-precision dicing saw. The results confirm that the improved algorithm could be used for high-precision and real-time dicing semiconductor chips.


Vision positioning Template matching Dicing Semiconductor chips 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


Funding information

This work is financially supported by the Science and Technology Project of Hunan Province (No. 2017WK2031).


  1. 1.
    Araujo LAO, FoschiniR CR, Jasinevícius G, Fortulan CA (2016) Precision dicing of hard materials with abrasive blade. Int J Adv Manuf Technol 86:2885–2894CrossRefGoogle Scholar
  2. 2.
    Wang Z, Gong S, Li D, Lu H (2017) Error analysis and improved calibration algorithm for LED chip localization system based on visual feedback. Int J Adv Manuf Technol 92:3197–3206CrossRefGoogle Scholar
  3. 3.
    Korman S, Reichman D, Tsur G, Avidan S (2017) Fast-match: fast affine template matching. Int J Comput Vis 121:111–125MathSciNetCrossRefGoogle Scholar
  4. 4.
    Cui DY, Gao WT, Xia KW (2017) Image denoising of strip steel surface defects based on K-SVD algorithm. Surface Technology 46:249–254. Google Scholar
  5. 5.
    Peng X, Xu J (2016) Hash-based line-by-line template matching for lossless screen image coding. IEEE T Image Process 25:5601–5609MathSciNetCrossRefGoogle Scholar
  6. 6.
    Bao G, Cai S, Qi L, Xun Y, Zhang LB, Yang QH (2016) Multi-template matching algorithm for cucumber recognition in natural environment. Comput Electron Agr 127:754–762CrossRefGoogle Scholar
  7. 7.
    Kim HY (2010) Rotation-discriminating template matching based on Fourier coefficients of radial projections with robustness to scaling and partial occlusion. Pattern Recogn 43:859–872CrossRefzbMATHGoogle Scholar
  8. 8.
    Feng Y, Ren J, Jiang J, Halvey M, Jose JM (2012) Effective venue image retrieval using robust feature extraction and model constrained matching for mobile robot localization. Mach Vision Appl 23:1011–1027CrossRefGoogle Scholar
  9. 9.
    Mattoccia S, Tombari F, Di Stefano L (2011) Efficient template matching for multi-channel images. Pattern Recogn Lett 32:694–700CrossRefGoogle Scholar
  10. 10.
    Yu X, Fei X (2017) Target image matching algorithm based on pyramid model and higher moments. J Comput Sci 21:189–194CrossRefGoogle Scholar
  11. 11.
    Liang J, Liao Z, Yang S, Wang Y (2012) Image matching based on orientation magnitude histograms and global consistency. Pattern Recogn 45:3825–3833CrossRefzbMATHGoogle Scholar
  12. 12.
    Matsuda Y, Miura N, Nagasaka A, Kiyomizu H, Miyatake T (2016) Finger-vein authentication based on deformation-tolerant feature-point matching. Mach Vision Appl 27:237–250CrossRefGoogle Scholar
  13. 13.
    Cui X, Kim H, Park E, Choi H (2013) Robust and accurate pattern matching in fuzzy space for fiducial mark alignment. Mach Vision Appl 24:447–459CrossRefGoogle Scholar
  14. 14.
    Yan W, Tian Z, Duan X, Pan L (2013) Feature matching based on unsupervised manifold alignment. Mach Vision Appl 24:983–994CrossRefGoogle Scholar
  15. 15.
    Yasseen Z, Verroust-Blondet A, Nasri A (2016) Shape matching by part alignment using extended chordal axis transform. Pattern Recogn 57:115–135CrossRefGoogle Scholar
  16. 16.
    Yang C, Tiebe O, Shirahama K, Grzegorzekl M (2016) Object matching with hierarchical skeletons. Pattern Recogn 55:183–197CrossRefGoogle Scholar
  17. 17.
    Zhong FQ, He SP, Li B (2017) Blob analyzation-based template matching algorithm for LED chip localization. Int J Adv Manuf Technol 93:55–63CrossRefGoogle Scholar
  18. 18.
    Wang ZY, Gong SH, Li DL, Lu HQ (2017) Error analysis and improved calibration algorithm for LED chip localization system based on visual feedback. Int J Adv Manuf Technol 92:3197–3206CrossRefGoogle Scholar
  19. 19.
    Kondo T (2014) Gradient orientation pattern matching with the Hamming distance. Pattern Recogn 47:3387–3404CrossRefGoogle Scholar
  20. 20.
    Brown LG (1992) A survey of image registration techniques. ACM Comput Surv 24:325–376CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Fengjun Chen
    • 1
    • 2
    Email author
  • Xiaoqi Ye
    • 1
  • Shaohui Yin
    • 1
    • 2
  • Qingshan Ye
    • 2
  • Shuai Huang
    • 1
  • Qingchun Tang
    • 1
  1. 1.National Engineering Research Center for High Efficiency GrindingHunan UniversityChangshaChina
  2. 2.Changsha Huateng Intelligent Equipment Co. Ltd.ChangshaChina

Personalised recommendations