The Development of Automated Solder Bump Inspection using Machine Vision Techniques

Visual inspection is an important task in the manufacturing processes for integrated circuit boards. In this paper, we focus on the solder bump inspection problem, and an automated visual inspection method using machine vision techniques is proposed. The solder bump inspection method consists of image grabbing, image preprocessing, feature extraction, and defect detection and classification. Five defect types of solder bumps to be inspected are bridging solder, excess solder, incomplete solder, non-wetting, and missing solder. The solder area, the number of edge pixels, the deviation from center, and the deformation ratio are used as the features for solder bump defect detection and classification. The proposed method is a hybrid algorithm, and it consists of two stages: the training stage and the inspection stage. The experimental results show that the proposed method is effective in detecting defects of solder bumps.