Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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QFN Solder Defect Detection Using Convolutional Neural Networks with Color Input Images

컬러 입력 영상을 갖는 Convolutional Neural Networks를 이용한 QFN 납땜 불량 검출

  • Kim, Ho-Joong (Department of Computer Engineering, Korea University of Technology and Education) ;
  • Cho, Tai-Hoon (School of Computer Science and Engineering, Korea University of Technology and Education)
  • 김호중 (한국기술교육대학교 컴퓨터공학과) ;
  • 조태훈 (한국기술교육대학교 컴퓨터공학부)
  • Received : 2016.08.16
  • Accepted : 2016.09.21
  • Published : 2016.09.30
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Abstract

QFN (Quad Flat No-leads Package) is one of the SMD (Surface Mount Device). Since there is no lead in QFN, there are many defects on solder. Therefore, we propose an efficient mechanism for QFN solder defect detection at this paper. For this, we employ Convolutional Neural Network (CNN) of the Machine Learning algorithm. QFN solder's color multi-layer images are used to train CNN. Since these images are 3-channel color images, they have a problem with applying to CNN. To solve this problem, we used each 1-channel grayscale image (Red, Green, Blue) that was separated from 3-channel color images. We were able to detect QFN solder defects by using this CNN. In this paper, it is shown that the CNN is superior to the conventional multi-layer neural networks in detecting QFN solder defects. Later, further research is needed to detect other QFN.

Keywords

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