| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系 === 90 === This paper applies computer vision and the improved Self-Organizing Map (SOM) network techniques to perform BGA measurement and defect inspection. The basic structure of the investigated system is, mainly, to automatically measure or inspect, one by one, the high solder ball, the solder ball diameter of the fine-pitched BGA structure and mounting pattern, solder ball roundness, solder ball density, ball offset, ball pitch, double ball, the characteristics of solder ball damage or ball missing, using the retrieved BGA original image, the optimal image separation with color pixel grouping and neural network methods, and finally digital image processing techniques.
In the aspects of investigation method and treating process of a BGA automated inspection system, we first need to separate solder balls from the solder pads, holes and green conductors in the original BGA image. Meanwhile, all noises must be filtered out. Consequentially, it is within the scope of this study to separate BGA solder balls from other images viewed as noises. The main purpose of this study is to filter out non-solder ball images and noises in the noise-corrupted BGA images, using neural network techniques and image filtering processes, to locate the solder ball coordinates within the regions in the retrieved full BGA image applying region point labeling, region analysis and edge detection of region pixels, and, then, to compute the size, diameter, ball pitch, area, and roundness-- whether they approach standard values-- of the solder balls using least error circle equation / best fit circle equation, signature, etc. Lastly, by matching the selected correlation coefficient, we inspect whether the solder ball density is defected. Combining the above-mentioned inspection results and applying various categories of defects as categorization standards, we clearly depict the quantities and categories of the defects and the locations of the defects in the original BGA image for the categorization reference for GOOD quality product.
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