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Logistic regression tree applied to classify PCB golden finger defects

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Abstract

Automatic inspection and classification of printed circuit board (PCB) defects remains a problem in the Integrated Circuit (IC) industry. In a small volume, large production variety environment, some of the widely used defects classification tools are the Bayes classifier, the linear discriminant function (LDF) classifier, the minimum distance classifier, and the K-nearest neighbour (K-NN) classifier. These classifiers all have shortcomings and their applications are limited. This study proposes the logistic regression tree as classifier where each node implements a logistic regression model to make a binary split, and draws a comparison with other classifiers. This experiment uses the Red-Green-Blue (RGB) values of an image with PCB golden fingers, as opposed to some features extracted from the image. The golden fingers defects considered in this study are scuffing, blotted tin, exposed nickel, and unplating. The result of this experiment shows that the logistic regression tree has an accuracy of 89.33%, while other classifiers can only achieve 81.67% to 87.17%. Furthermore, the decision flexibility and the result stability show that the logistic regression tree is an excellent choice as a classifier.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, Yuan Ze University, No. 135, Yuan Tung Road, Nei-Li, Tao-Yuan, Taiwan, 320

    B.C. Jiang

  2. Department of Industrial Engineering and Management, Ming Chi Institute of Technology, Taiwan

    C.C. Wang

  3. World Wiser Electronics Inc., Taiwan

    P.L. Chen

Authors
  1. B.C. Jiang
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  2. C.C. Wang
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  3. P.L. Chen
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Corresponding author

Correspondence to B.C. Jiang.

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Jiang, B., Wang, C. & Chen, P. Logistic regression tree applied to classify PCB golden finger defects. AMT 24, 496–502 (2004). https://doi.org/10.1007/s00170-002-1500-2

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  • DOI: https://doi.org/10.1007/s00170-002-1500-2

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