Defects pattern recognition for flip-chip solder joint quality inspection with laser ultrasound and Interferometer

Author

Liu, Sheng ; Ume, Charles I. ; Achari, Achyuta

Author_Institution

Sch. of Mech. Eng., Georgia Inst. of Technol., Atlanta, GA, USA

Volume

27

Issue

1

fYear

2004

Firstpage

59

Lastpage

66

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 and can, therefore, find differences between good and bad chips, as well as classifying the type of defect.

Keywords

feature extraction; flip-chip devices; inspection; interferometers; neural nets; pattern classification; probability; soldering; ultrasonic measurement; cluster analysis; dominant frequency; error ratio; feature vectors; flip-chip solder joint; interferometer; laser ultrasound; neural network classification; pattern recognition; probabilistic neural network; quality inspection; ultrasound waveforms; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Flip chip solder joints; Frequency; Inspection; Neural networks; Pattern recognition; Performance analysis; Ultrasonic imaging;

fLanguage

English

Journal_Title

Electronics Packaging Manufacturing, IEEE Transactions on

Publisher

ieee

ISSN

1521-334X

Type

jour

DOI

10.1109/TEPM.2004.830515

Filename

1331576