Learning algorithms for both real-time detection of solder shorts and for SPC measurement correction using cross-sectional x-ray images of PCBA solder joints

Paul A. Roder

doi:10.1117/12.171225

11 March 1994 Learning algorithms for both real-time detection of solder shorts and for SPC measurement correction using cross-sectional x-ray images of PCBA solder joints

Paul A. Roder

Author Affiliations +

Proceedings Volume 2183, Machine Vision Applications in Industrial Inspection II; (1994) https://doi.org/10.1117/12.171225
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States

Abstract

Learning algorithms are introduced for use in the inspection of cross-sectional X-ray images of solder joints. These learning algorithms improve measurement accuracy by accounting for localized shading effects that can occur when inspecting double- sided printed circuit board assemblies. Two specific examples are discussed. The first is an algorithm for detection of solder short defects. The second algorithm utilizes learning to generate more accurate statistical process control measurements.

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Paul A. Roder "Learning algorithms for both real-time detection of solder shorts and for SPC measurement correction using cross-sectional x-ray images of PCBA solder joints", Proc. SPIE 2183, Machine Vision Applications in Industrial Inspection II, (11 March 1994); https://doi.org/10.1117/12.171225

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