Automatic classification of electronic components based on the support vector machine

Paweł Fidelus, Jerzy Martyna


In this paper, we present a classification of electronic components in the electronic factory. This classification provides relevant information for correcting the manufacturing process, thereby enhancing the production fields and the quality of product. Our classification system based on the support vector machine (SVM) classifies all the used electronic components into predefined categories that are learnt from the training samples. The system has been deployed in the manufacturing line and has met the design criteria of over 90% of the classification rate and 80% of the classification accuracy.


support vector machine; classification


Burges C.: A Tutorial on Support Vector Machines for Pattern Recognition, [in:] Fayyad U. (ed.): Knowledge Discovery and Data Mining. Dordrecht, Kluwer 2000, p. 1÷43.

Burggraf F.: Defect Inspection: Wafers In, Process Control Out. Semiconductor International, Vol. 14, 1991, p. 58÷62.

Chou P. B., Ravishankar Rao A., Sturzenbecker M. C., Wu F. Y., Brecher V. H.: Automatic Defect Classification for Semiconductor Manufacturing. Machine Vision and Application, Vol. 9, 1997, p. 201÷214.

Chin R.: Survey of Automated Visual Inspection: 1981 to 1987. Computer Vision Graphics and Image Processing, Vol. 41, 1988, p. 346÷381.

Cortes C., Vapnik V. N.: Support-Vector Networks. Machine Learning, Vol. 20, No. 3, 1995, p. 273÷297.

Crammer K., Singer Y.: On the Learnability and Design of Output Codes for Multiclass Problems. Int. Conf. Computational Learning Theory, 2000, p. 35÷46.

Dom B., Brecher V. H.: Recent Advances in Inspecting Integrated Circuits for Pattern Defects. IBM Research Report, RJ 9602, Yorktown Heights, N.Y. 1993.

Stinson G.: Applications of Automatic Defect Classification in Photolitography. Advanced Semiconductor Manufacturing Conference and Workshop, IEEE/SEMI, 1999, p. 270÷274.

Vapnik V. N.: The Nature of Statistical Learning Theory. Springer, Berlin-Heidelberg-New York 1995.

Vapnik V. N.: Statistical Learning Theory. John Wiley & Sons, New York 1998.

Vapnik V. N., Chervonenkis A. Ya.: On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities. Soviet Math. Dokl., Vol. 9, 1968, p. 915÷918.