NEURAL-NETWORK-BASED INSPECTION OF SOLDER JOINTS USING A CIRCULAR ILLUMINATION

In this paper, we describe an approach to inspection of solder joints on printed circuit boards by using a circular illumination technique a nd a neural network classifier. The illumination technique, consisting of three tiered circular colour lamps and one colour camera, gives go od visual cues to infer 3D shape of the solder joint surface. A genera l aspect of this inspection is that the shape of the solder joint tend s to greatly vary according to soldering conditions. Due to this, a ne ural network classifier based on a supervised version of Kohonen learn ing vector quantization (LVQ) is proposed to automatically and efficie ntly make classification criteria of the solder joint shapes according to their quality. The practical feasibility of the proposed approach is demonstrated by building a prototype inspection machine and testing its performance.