Context-Sensitive Filtering in RLE for PCB Inspection

This paper presents a fast printed circuit board (PCB) inspection methodology based on the segmentation of the PCB image into basic patterns and context sensitive filtering of the difference image for functional defect detection. The system consists of three parts: (i) segmentation of the artwork and test board images into basic patterns using the CAD data for the artwork, (ii) comparison of the test board image windows with the corresponding artwork image windows, (iii) context-sensitive filtering of the difference image obtained in step (ii) for the determination of the defects and their locations. For image representation, runlength encoding (RLE) is used and processing is done directly on RLE data. This provides the needed efficiency in processing huge amounts of image data, which is typical in PCB inspection. Working on the difference image results in considerable reduction in the data to be processed during the filtering stage. Context-sensitive filtering enables classification of defects in such a way that only potentially functional defects are caught and reported in the final analysis.