Automatic Detection and Location for The Fiducial Marks and Reference Fiducial Marks

Muh-Don Hsiao, CHUEN-HORNG Lin, Jr-Wei Chen


Automatic detection and location are proposed for the fiducial marks (FM) and reference fiducial marks (RFM) for the LED wafer images in this paper. The LED wafer image has two FMs, where the obvious FM is in the upper layer and the obscure FM is in the lower layer. The upper RFM is automatically detected and the lower RFM is determined after using an image enhancement technique. The automated search FMs of LED wafer images are divided into four steps: rough search, FM matching, fine search and trimming for sub-pixel images. FM matching includes the sum of absolute differences (SAD) and boundary feature matching (BFM). There are two types of experiments on an LED wafer image. In the first type, the upper and lower FMs of an image are circular. In the second type, the upper FM of an image is crossed, while the lower FM of an image is circular. The result shows that if the difference between RFM and FM grey-scale value distributions is small, the SAD matching has good effect. However, if the difference is large, the BFM matching is better. The positioning trimming for sub-pixels has a better effect on low resolution images. To validate the effect of the proposed method, the results are compared with the results of manual image matching in this study. By comparing the experimental results with those of the manual measurement method, we find that there are smaller error levels and a better effect.


fiducial mark, rough search, fine search, LED wafer

Full Text:



M. Tichem and M. S. Cohen, “Subμm Registration of Fiducial Marks Using Machine Vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16(8), pp.791-794, 1994.

H. K. Nishihara and P. A. Crossley, “Measuring Photolithographic Overlay Accuracy and Critical Dimensions by Correlating Binarized Laplacian of Gaussian Convolutions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10(1), pp.17-30, 1988.

X. Fernandez and J. Amat, “Research on Small Fiducial Mark Use for Robotic Manipulation and Alignment of Ophthalmic Lenses,” 7th IEEE International Conference on Emerging Technologies and Factory Automation, vol. 2, pp. 1143 -1146, 1999.

N. Guil, J. Villalba, and E. L. Zapata,“ A Fast Hough Transform for Segment Detection,” , IEEE Transactions on Image Processing, vol. 4(11), pp.1541-1548, 1995.

S. K. Tsau, D. Y. Hong, H. W. Lee , C. M. Chang, and C. H. Lin, “Multiple Alignment Stage for the Automatic Precision Alignment System, ” International Symposium on Computer, Consumer and Control, pp.926-929, 2012.

Y. C. Lin, Y. Y. Chiu, H. W. Lee, B. Y. Jhan, and C. H. Lin, “The Study of Automate Locate Special Fiducial Marks,” The Sixth International Conference on Genetic and Evolutionary Computing, 2012.

R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” Prentice-Hall, 2002.

J. F. Canny, “A Computational Approach to Edge Detection,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 8(6), pp. 679–698, 1986.

L .Ding and A. Goshtasby, “On the Canny Edge Detector,” Pattern Recognition, vol. 34, pp. 721–725, 2001.

C. H. Lin, Y. K. Chan, and C. C. Chen, “Detection and Segmentation of Cervical Cell Cytoplast and Nucleus,” International Journal of Imaging Systems and Technology, Vol. 19, pp. 260-270, 2009.

F. A. Pellegrino, W. Vanzella, and V. Torre, “Edge Detection Revisited,” IEEE Transactions on Systems, Man, and Cybernetics-part B: CYBERNETICS, vol. 34(3), pp.1500-1518, 2004.

C. H. Lin and Y. J. Syu, “Fast Segmentation of Porcelain Images Based on Texture Features,” Journal of Visual Communication and Image Representation, Vol. 21, pp. 707-721, 2010.

J. Haddon and J. Boyce, “Image Segmentation by Unifying Region and Boundary Information,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12(10), pp. 929-948, 1990.

C. H. Lin and C. C. Chen, “Image Segmentation Based on Edge Detection and Region Growing for ThinPrep-Cervical Smear,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 24(7), pp. 1061-1089, 2010.

Z. Hou, Q. Hu, and W. L. Nowinski, “On Minimum Variance Thresholding,” Pattern Recognition Letters, vol. 27, pp. 1732-1743, 2006.

F. Y. Shih and S. Cheng, “Automatic Seeded Region Growing for Color Image Segmentation,” Image and Vision Computing, vol. 23, pp. 877-886, 2005.

D. Mumford and J. Shah, “Optimal Approximations by Piecewise Smooth Function and Associated Variational Problems,” Communications on Pure and Applied Mathematics, vol. 42, pp.577-684, 1989.

B. Robbins and R. Owens, ”2D Feather Detection via Local Energy,“ Image and Vision Computing , vol. 15(5), pp. 353-368, 1997.

T. Miroslav and H. Mark, ”Fast Corner Detection,“ Image and Vision Computing, vol. 16(2), pp.75-87,1998.

M. Z. Brown, D. Burschka, and G. D. Hager, “Advances in Computational Stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25(8), pp. 993-1008, 2003.

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions of Systems, Man, and Cybernetics, vol. 9, pp. 62-66, 1979.

K. Jensen and D. Anastassiou, “Subpixel edge localization and the interpolation of still images,” IEEE Transactions on Image Processing, Vol.4, pp. 285-295, 1995

M. Sonka, V. Hlavac, and R. Boyle, “Image Porcessing, Analysis, and Machine Vision,” PWS Pubishing, 1999.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.