Development and Characterization of Pattern Recognition Algorithm for Defects in Semiconductor Packages

  • Kim, Jae-Yeol (Department of mechatronics engineering, Chosun University) ;
  • Yoon, Sung-Un (Department of mechanical engineering, Chosun University) ;
  • Kim, Chang-Hyun (Department of precision mechanical engineering, Graduate school, Chosun University)
  • Published : 2004.07.01

Abstract

In this paper, the classification of artificial defects in semiconductor packages is studied by using pattern recognition technology. For this purpose, the pattern recognition algorithm includes the user made MATLAB code. And preprocess is made of the image process and self-organizing map, which is the input of the back-propagation neural network and the dimensionality reduction method, The image process steps are data acquisition, equalization, binary and edge detection. Image process and self-organizing map are compared to the preprocess method. Also the pattern recognition technology is applied to classify two kinds of defects in semiconductor packages: cracks and delaminations.

Keywords

References

  1. Kim, Jae-Yeol, 'A Study on the Image Processing of Micro-defects Detection of Semiconductor Package by Ultrasonic Wave,' Ph. D thesis, Hanyang University, 1990
  2. Lawrence, C. Steve, and Lee, Giles and Tsoi, Ah Chung and Back, D. Andrew, 'Face Recognition: A Convolutional Neural Network Approach,' IEEE Transaction on Neural Network, Special Issue on Neural Network and Pattern Recognition, Vol. 8, No. 1, pp. 98 - 113, 1997
  3. Master, Timothy, 'Signal and Image Processing with Neural Networks,' John Wiley & Sons, 1994.
  4. Kohonen, T., 'The self-organizing map,' Proceedings of the IEEE, Vol. 78, pp. 1464 - 1480, 1990 https://doi.org/10.1109/5.58325
  5. Otsu, Nobuyuki, 'A Threshold Selection Method from Gray-Level Histograms,' IEEE Transaction on Systems, Men, and Cybernetics, Vol. SMC-9, No. 1, pp. 41 - 47, 1986
  6. Tompson, R. B., 'Quantitative Ultrasonic Non-destructive Evaluation Methods,' Journal of Applied Mechanics, Vol. 50, pp. 1991 - 1201, 1983
  7. Hall, E. L., 'A Survey of Preprocessing and Feature Extraction Techniques for Radiographic Image,' IEEE Transaction on Computation, Vol. C-20, No. 9, pp. 1032 - 1044, 1971 https://doi.org/10.1109/T-C.1971.223399
  8. Jhang, K. Y. and Jang, H. S. and Park, P. Y., 'Separation of Superimposed Pulse-Echo Signal for Improvement of Resolution of Scanning Acoustic Microscope,' Journal of the KSPE, Vol. 17, No. 7, pp. 217 - 225, 2000
  9. Kim, J. Y. and Hong, W. and Han, J. H., 'A Study on the Detection of Interfacial Defect to Boundary Surface in Semiconductor Packages by Ultrasonic Signal Processing,' Journal of KSNT, Vol. 19, No. 5, pp. 369 - 377, 1999
  10. Sim, J. H. and Kweon, H. J. and Paik, I. H., 'Identification of the Chip Form Using Neural Network,' Journal of the KSPE, Vol. 15, No. 12, pp. 517 - 112, 1998
  11. Choi, M. Y. and Park, I. G. and Han, E. K., 'Measurement of Defects with Scanning Acoustic Microscope and Acoustic Emission,' Journal of the KSPE, Vol. 8, No. 4, pp. 118 - 125, 1991