An optical solder joint inspection system (OSJIS) has been developed for the automatic visual inspection of soldered parts on the printed circuit boards. Its advantages over existing techniques include the detection of 3-D shape of specular objects with high reliability and high speed. We propose a solder joint inspection scheme for a prototype of the OSJIS. The inspection scheme is composed of two steps: feature extraction and classification. In the feature extraction step, by scanning a laser beam over the area of solder joints the system obtains two orientation curves representing the quality of soldering condition, and then nine features are extracted from the curves. In the classification step, a neural network classifies the solder joint according to the application requirements by using the features. Experiments were performed for SOP (small outline package)s and QFP (quad flat package)s in insufficient, normal and excess soldering condition. Based upon observation of the experimental results, the proposed inspection scheme shows excellent consistency with visual inspection and a good accuracy of classification performance of 94.2%.
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