Pattern classification of solder joint images using a correlation neural network

This paper presents a method of classifying solder joints on printed-circuit boards (PCB), using a neural-network approach. Inherently, the surface of the solder joints is curved, tiny and specularly reflective; it induces a difficulty of taking good images of the solder joints. The shapes of the solder joints tend to vary greatly with soldering conditions; solder joints, even when classified into the same soldering quality, have very different shapes. Furthermore, the position of the joints is not consistent within a registered solder pad on the PCB. Due to these aspects, it has been difficult to determine the visual features and classification criteria for automatic solder-joint inspection. In this research, the solder joints, imaged by using a circular, tiered illumination system of three colored lamps, are represented as red, green and blue colored patterns, showing their surface-slopes. Cross-correlation and auto-correlation of the colored patterns are used to classify the 3D shapes of the solder joints by their soldering qualities. To achieve this, a neural network is proposed, based on a functional link net, with two processing modules. The first preprocessing module is designed to implement the calculation of the correlations in functional terms. The subsequent, trainable module classifies the solder joints, based upon the capability learned from a human supervisor. The practical feasibility of the proposed method is demonstrated by testing numerous commercially manufactured PCBs.