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Abstract
The existing inspection systems are algorithmically slow and suffer from many drawbacks in meeting the inspection requirements of printed circuit board fabrication. The system presented in this paper can handle all of the defects simultaneously with the same approach and is significantly faster compared to the existing approaches. The system consists of three major phases: the first step is the segmentation of the golden PCB image into basic subpatterns, the second step is the learning phase, and the third and final step is the verification/inspection phase. The system presented here introduces the application of neural networks and fuzzy logic in printed circuit board inspection. The method is highly parallel and works at the subpattern level. Experimental results which demonstrate the effectiveness of the proposed algorithms are given.
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