Abstract
We describe an approach to automation of visual inspection of ball grid array (BGA) solder joint defects of surface mounted components on printed circuit boards by using a neural network. Inherently, the BGA solder joints are located below its own package body, and this induces a difficulty in taking a good image of the solder joints when using a conventional imaging system. To acquire the cross-sectional image of a BGA solder joint, an X-ray cross-sectional imaging method such as laminography and digital tomosynthesis is utilized. However an X-ray cross-sectional image of a BGA solder joint, using laminography or DT methods, has inherent blurring effect and artifact. This problem has been a major obstacle to extracting suitable features for classification. To solve this problem, a neural network based classification method is proposed. The performance of the proposed approach is tested on numerous samples of printed circuit boards and compared with that of a human inspector. Experimental results reveal that the proposed method shows practical usefulness in BGA solder joint inspection.
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