Cover
Vol. 2 No. 1 (2006)

Published: July 31, 2006

Pages: 7-18

Original Article

FEATURE EXTRACTION METHODS FOR IC CHIP MARKING INSPECTION-A COMPARISON

R. Nagarajan 1 Sazali Yaacob Paulraj Pandian Mohamed Rizon nan M.Karthigayan
1School of Mechatronics, Northern Malaysia University College of Engineering, 01000 Kangar, Malaysia
History
  • Received: May 31, 2006
  • Available Online: July 31, 2006
DOI

https://doi.org/10.37917/ijeee.2.1.2

Abstract

In this paper, an Industrial machine vision system incorporating Optical Character Recognition (OCR) is employed to inspect the marking on the Integrated Circuit (IC) Chips. This inspection is carried out while the ICs are coming out from the manufacturing line. A TSSOP-DGG type of IC package from Texas Instrument is used in the investigation. The inspection has to identify the print errors such as illegible character, missing characters and up side down printing. The vision inspection of the printed markings on the IC chip is carried out in three phases namely image preprocessing, feature extraction and classification. Projection profile and Moments are employed for feature extraction. A neural network is used as a classifier to detect the defectively marked IC chips. Both feature extraction methods are compared in terms of marking inspection time.

Keywords

References

  1. Mantas. J, "An Overview of Character Recognition Methodologics", Pattern Recognition, V01.19, No.6, pp.425-430,1986.
  2. Shunji Mori, Ching Y. Suen and Kazuhiko Yamamoto, "Historical Review of OCR Research and Development", Proceedings of the ΙΕΕΕ, νο180 (7), July 1992.
  3. Adnan Amin, Humoud Al-Sadoun and Stephen Fischer, "Hand-Printed Arabie Character Recognition System Using An Neural Network", Pattern recognition, vol.29(4), pp 663-675, 1996.
  4. Nagarajan.R, Sazali Yaacob, Paulraj Pandian, Karthigayan. M, Shamsudin Hj Amin and Marzuki Khalid, "A Neural Network Application for Inspecting The Marking of Symbols on Integrated Circuit Chips", The Seventh Triennial AEESEAP Conference Enhancing Engineering Education and Training, University of Malaya, Kuala Lumpur, Malaysia, 8-9 December, 2003.
  5. Wumo Pan, Jianming Jin, Guangshun Shi and Q.R. Wang, "A System for Automatic Chinese Business Card Recognition "Proceedings of the Sixth International Conference on Document Analysis and recognition, IEEE Computer society, pp.577- 581, September, 2001.
  6. Rafael C. Gonzalez and Richard E. Woods, Digital Image processing, Longman, Inc, Addison-Wesley September 1993.
  7. Mauritius Seeger and Christopher Dance, "Binarising Camera Images for OCR", Document Analysis and Recognition, Proceedings. Sixth International Conference, Pages:54-58, September 2001.
  8. Abdelwahab Zrandini and Rolf Ingold "Optical Font Recognition From Projection Profiles" Electronic Publishing, Vol.6(3), pp-249-260 September 1993.
  9. Oivind Due Trier, Anil K.Jain and Torfinn Taxt, "Feature Extraction Methods for Character Recognition- A Survey", Pattern Recognition, Vol.29, No.4, pp.641-662,1996.
  10. Sivanandam. S.N and Paulraj.M, "Introduction FO Artificial Neural Network", Vikas Publishing House, Chennai, 2003.
  11. Laurene Fausett, Fundamentals of Neural Networks, Prentice Hall Publishing, 1994.
  12. Bernard Widrow and Michael A. Lehr, " 30 years of Adaptive Neural Network Proceptron, Madeline and Backpropagation", Proceedings from IEEE, vol. 78(9), pp 1415-1442, Sept 1990.
  13. Clifford Lau, Neural Networks- Theoretical Foundation and analysis, IEEE press, New York, 1992.
  14. Powalka. R.K, Sherkat. N and Whitrow. R. J. 1995. Zoning Invariant Wholistic Recognizer for Hybrid Recognition of Handwriting. Proccedings of Third International Conference on Document Analysis and Recognition. Vol.1.
  15. Adrian Ciobanu, Hamid Shahbazkia and Hans Du Buf 2000. "Contour Profiling by Dynamic Ellipse Fitting", Proceedings of 15th International Conference on Pattern Recognition. vol. 3, pp. 750-753, 2000.