Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Hybrid Neural Network Based BGA Solder Joint Inspection Using Digital Tomosynthesis

하이브리드 신경회로망을 이용한 디지털 단층 영상의 BGA 검사

  • 고국원 (한국과학기술원 기계공학과) ;
  • 조형석 (한국과학기술원 기계공학과) ;
  • 김종형 (삼성전자 생산기술센터) ;
  • 김형철 (삼성전자 생산기술센터)
  • Published : 2001.03.01
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Abstract

In this paper, we described an approach to the automation of visual inspection of BGA solder joint defects of surface mounted components on printed circuit board by using neural network. Inherently, the BGA solder joints are located underneath its own package body, and this induces a difficulty of taking good image of the solder joints by using conventional imaging systems. To acquire the cross-sectional image of BGA sol-der joint, X-ray cross-sectional imaging method such as laminography and digital tomosynthesis has been cur-rently utilized. However, the cross-sectional image obtained by using laminography or DT methods, has inher-ent blurring effect and artifact. This problem has been a major obstacle to extract suitable features for classifi-cation. To solve this problem, a neural network based classification method is proposed int his paper. The per-formance of the proposed approach is tested on numerous samples of printed circuit boards and compared with that of human inspector. Experimental results reveal that the method provides satisfactory perform-ance and practical usefulness in BGA solder joint inspection.

Keywords

References

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