Title :
Defects pattern recognition for flip chip solder joint quality inspection with laser ultrasound and interferometer
Author :
Sheng Liu ; Ume, I.C.
Author_Institution :
Sch. of Mech. Eng., Georgia Inst. of Technol., Atlanta, GA
fDate :
6/24/1905 12:00:00 AM
Abstract :
A defects pattern recognition system has been developed for the flip chip solder joint quality inspection by using laser ultrasound and interferometric techniques. This system extracts Error Ratio and dominant frequency as features from ultrasound waveforms. It also performs a cluster analysis of those feature vectors by applying probabilistic neural network classification algorithm. The system can automatically classify chips into different clusters, therefore can find differences between good and bad chips, as well as classifying the type of defect.
Keywords :
feature extraction; flip-chip devices; inspection; light interferometry; neural nets; pattern classification; pattern clustering; quality control; ultrasonic applications; cluster analysis; defects pattern recognition; error ratio; feature vectors; flip chip; joint quality inspection; laser interferometer; laser ultrasound; probabilistic neural network classification; solder joint quality; ultrasound waveforms; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Flip chip solder joints; Frequency; Inspection; Neural networks; Pattern recognition; Performance analysis; Ultrasonic imaging;
Conference_Titel :
Electronic Components and Technology Conference, 2002. Proceedings. 52nd
Print_ISBN :
0-7803-7430-4
DOI :
10.1109/ECTC.2002.1008303
