DocumentCode :
1965395
Title :
Fast and Accurate Automatic Defect CLuster Extraction for Semiconductor Wafers
Author :
Ooi, Melanie Po-Leen ; Chan, Chris ; Tee, Wey Jean ; Kuang, Ye Chow ; Kleeman, Lindsay ; Demidenko, Serge
Author_Institution :
Sch. of Eng., Monash Univ., Melbourne, VIC, Australia
fYear :
2010
fDate :
13-15 Jan. 2010
Firstpage :
276
Lastpage :
280
Abstract :
Reduction in integrated circuit (IC) half technology, which will no longer be sustainable by traditional fault isolation and failure analysis techniques. There is an urgent need for diagnostic software tools with (which manifest as clusters) observed from manufacturing defects can be traced back to a specific process, equipment or technology, a novel data mining algorithm defects from test data logs. This algorithm and provides accurate detection of 99%.
Keywords :
semiconductor device manufacture; wafer bonding; automatic defect cluster extraction; data mining algorithm; diagnostic software tools; semiconductor wafers; Circuit faults; Clustering algorithms; Data mining; Failure analysis; Integrated circuit technology; Isolation technology; Manufacturing processes; Semiconductor device manufacture; Software algorithms; Software tools; clusters; data mining; defects; detection; segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic Design, Test and Application, 2010. DELTA '10. Fifth IEEE International Symposium on
Conference_Location :
Ho Chi Minh City
Print_ISBN :
978-0-7695-3978-2
Electronic_ISBN :
978-1-4244-6026-7
Type :
conf
DOI :
10.1109/DELTA.2010.66
Filename :
5438675
Link To Document :
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