The increasing packaging density of flip chips brings great
challenges for micro defect detection. It is necessary to develop an
efficient and automatic inspection system for electronic industrial
application. In this paper, the scanning acoustic microscopy (SAM)
technology and the optimized classifier were investigated for the
automatic detection of solder joint defects of flip
chips. Ultrasonic images of 1902 solder bumps from 6 chip samples
were obtained using a SAM equipment. The decision tree model was
improved by introducing the granularity decision entropy. The
feature vectors were extracted from the images of solder bumps, and
then used for classification. The results show that the decision
tree model correctly detected 1812 solder bumps with the accuracy of
95.3%. It is verified that the ultrasonic defect detection system
based on the improve decision tree model has high accuracy for micro
defect inspection, which has potential and promising application in
the electronic packaging industry.
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