Application of Back-Propagation Neural Network for BGA Inspection System

• Main Authors: Chen Wei-Han, 陳維翰
• Other Authors: Tsai Min-Jong
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/64028445868082588821

碩士 === 國立臺灣科技大學 === 自動化及控制研究所 === 92 === The main purpose of this research is to apply back-propagation neural network and computer vision to develop a two-dimension BGA (Ball Grid Array) defect inspection system. By using this system, the automatic inspection via computer vision can reduce the human error. The developed inspection functions include ball position offset, ball size and ball shape. In this research, the information of solder balls can be obtained through the following steps: the image grabbing of solder balls, median filtering, the binary image by using the Otsu’s method, morphology image processing, blob analysis, subpixel edge-detect, and the best fit ellipse equation. The acquired information of each solder ball, such as center, area and axis length were processed into center offset, area ratio and axis ratio of ellipse for neural network use. Those data were used to train the back-propagation neural network. The coordinate processing was also considered to overcome the problems while the BGA with random shift or rotation. After the back-propagation neural network has been trained successfully, it can be used to inspect the solder balls to be examined. According to the self-learning and highly recognized capability of the BPNN, this BGA inspection system is accurate for inspecting the defect of solder balls. The system still can inspect correctly even though the BGA with shift or angle of rotation.