Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
권호 표지

Adjustment Algorithms for the Measured Data of Stereo Vision Methods for Measuring the Height of Semiconductor Chips

반도체 칩의 높이 측정을 위한 스테레오 비전의 측정값 조정 알고리즘

  • Kim, Young-Doo (School of Computer Engineering, Korea University of Technology and Education) ;
  • Cho, Tai-Hoon (School of Computer Engineering, Korea University of Technology and Education)
  • 김영두 (한국기술교육대학교 컴퓨터공학부) ;
  • 조태훈 (한국기술교육대학교 컴퓨터공학부)
  • Received : 2011.05.09
  • Accepted : 2011.06.15
  • Published : 2011.06.30
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Abstract

Lots of 2D vision algorithms have been applied for inspection. However, these 2D vision algorithms have limitation in inspection applications which require 3D information data such as the height of semiconductor chips. Stereo vision is a well known method to measure the distance from the camera to the object to be measured. But it is difficult to apply for inspection directly because of its measurement error. In this paper, we propose two adjustment methods to reduce the error of the measured height data for stereo vision. The weight value based model is used to minimize the mean squared error. The average value based model is used with simple concept to reduce the measured error. The effect of these algorithms has been proved through the experiments which measure the height of semiconductor chips.

Keywords

References

  1. G. Sansoni, "State of The Art and Application of 3D Imaging Sensors in Industry, Cultural Heritage, Medicine, and Criminal Investigation," Sensors, 9, pp. 568-601, 2009. https://doi.org/10.3390/s90100568
  2. H.-N Yen and D.-M. Tsai, "A fast full-field 3D measurement system for BGA coplanarity inspection," Int J Adv Manuf Technol, 24, 070-7865-4264 132-139, 2004.
  3. T. Watanabe and A. Kusano, "3D Precise Inspection of Electronic Devices by Single Stereo Vision," MVA2007 IAPR Conference on Machine Vision Application, May 16-18, 2007, Tokyo, JAPAN.
  4. Z. Zhang, "A Flexible New Technique for Camera Calibration," in Proc. of the IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), pp. 1330-1334, 2000. https://doi.org/10.1109/34.888718
  5. R. Hartley, "Theory and Practice of Projective Rectification," International Journal of Computer Vision, 35(12), pp. 115-127, 1999. https://doi.org/10.1023/A:1008115206617
  6. R. A. Lande and N. A. Thacker, "Tutorial: Overview of Stereo Matching Research," from Tina Memos: Human and Machine Vision.
  7. B. Widrow and M. Kamenetsky, "Statistical Efficiency of Adaptive Algorithms," Neural Networks, 16, pp. 735-744, 2003. https://doi.org/10.1016/S0893-6080(03)00126-6
  8. M. Givens, "Enhanced-Convergence Normalized LMS Algorithm," IEEE Signal Processing Magazine, 26(3), pp. 81-95, 2009. https://doi.org/10.1109/MSP.2009.932168
  9. T. Aboulnasr and K. Mayyas, "A Robust Variable Step-Size LMS-Type Algorithm: Analysis and Simulation," in Proc. of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2, pp. 1408-1411, 1995.
  10. J. Y. Bouguet, "Camera Calibration Toolbox for Matlab".
  11. D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, 60, pp. 91-110, 2004.
  12. R. Hartley and A. Zisserman, "Multiple View Geometry in Computer Vision," Cambridge, UK: Cambridge University Press, 2006.