Electronic hidden solder joint geometry characterization

Sheng-Jen Hsieh

doi:10.1117/12.819383

22 April 2009 Electronic hidden solder joint geometry characterization

Sheng-Jen Hsieh

Proceedings Volume 7299, Thermosense XXXI; 72990E (2009) https://doi.org/10.1117/12.819383
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

To reduce the size of electronic equipment, multi-layer printed circuit board structures have become popular in recent years. As a result, the inspection of hidden solder joints between layers of boards has become increasingly difficult. Xray machines have been used for ball grid array (BGA) and hidden solder joint inspection; however, the equipment is costly and the inspection process is time consuming. In this paper, we investigate an active thermography approach to probing solder joint geometry. A set of boards having the same number of solder joints and amount of solder paste (0.061 g) was fabricated. Each solder joint had a different geometry. A semi-automated system was built to heat and then transfer each board to a chamber where an infrared camera was used to scan the board as it was cooling down. Two-thirds of the data set was used for model development and one-third was used for model evaluation. Both artificial neural network (ANN) and binary logistic regression models were constructed. Results suggest that solder joints with more surface area cool much faster than those with less surface area. In addition, both modeling approaches are consistent in predicting solder geometry; ANN had 85% accuracy and the regression model had 80%. This approach can potentially be used to test for cold solder joints prior to BGA assembly, since cold solder joints may have air gaps between the joint and the board and air is a poor heat conductor. Therefore, a cold solder joint may have a slower cooling rate than a normal one.

Citation Download Citation

Sheng-Jen Hsieh "Electronic hidden solder joint geometry characterization", Proc. SPIE 7299, Thermosense XXXI, 72990E (22 April 2009); https://doi.org/10.1117/12.819383

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KEYWORDS

Thermal modeling

Data modeling

Binary data

Inspection

Neural networks

Thermography

Evolutionary algorithms

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