Applying the support vector machine with optimal parameter design into an automatic inspection system for classifying micro-defects on surfaces of light-emitting diode chips

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This study discusses the optimal design of an automatic inspection system for processing light-emitting diode (LED) chips. Based on support vector machine (SVM) with optimal theory, the classifications of micro-defects in light area and electrode area on the chip surface, and develop a robust classification module will be analyzed. In order to design the SVM-based defect classification system effectively, the multiple quality characteristics parameter design. The Taguchi method is used to improve the classifier design, and meanwhile, PCA is used for analysis of multiple quality characteristics on influence of characteristics on multi-class intelligent classifier, to regularly select effective features, and reduce classification data. Aim to reduce the classification data and dimensions, and with features containing higher score of principal component as decision tree support vector machine classification module training basis, the optimal multi-class support vector machine model was established for subdivision of micro-defects of electrode area and light area. The comparison of traditional binary structure support vector machine and neural network classifier was conducted. The overall recognition rate of the inspection system herein was more than 96%, and the classification speed for 500 micro-defects was only 3 s. It is clear that we have effectively established an inspection process, which is highly effective even under disturbance. The process can realize the subdivision of micro-defects, and with quick classification, high accuracy, and high stability. It is applicable to precise LED detection and can be used for accurate inspection of LED of mass production effectively to replace visual inspection, economizing on labor cost.

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The research was supported by the Ministry of Science & Technology of the Republic of China under the Grant No. MOST 104-2221-E-011-156.

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Correspondence to Chung-Feng Jeffrey Kuo.

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Kuo, C.J., Tung, C. & Weng, W. Applying the support vector machine with optimal parameter design into an automatic inspection system for classifying micro-defects on surfaces of light-emitting diode chips. J Intell Manuf 30, 727–741 (2019).

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  • Taguchi methods
  • Principal component analysis
  • Decision tree support vector machine
  • Multi-class support vector machine model