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An automated visual inspection of solder joints using 2D and 3D features

In this paper, efficient techniques for solder joint inspection have been described. Using three layers of ring shaped LED's with different illumination angles, three frames of images are sequentially obtained. From these images the regions of interest (soldered regions) are segmented, and their characteristic features including the average gray level and the percentage of highlights-referred to as 2D features-are extracted. Based on the backpropagation algorithm of neural networks, each solder joint is classified into one of the pre-defined types. If the output value is not in the confidence interval, the distribution of tilt angles-referred to as 3D features-is calculated, and the solder joint is classified based on the Bayes classifier. The second classifier requires more computation while providing more information and better performance. The proposed inspection system has been implemented and tested with various types of solder joints in SMDs. The experimental results have verified the validity of this scheme in terms of speed and recognition rate.