A neural network approach to the inspection of ball grid array solder joints on printed circuit boards

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