Title of article :
Defect cluster recognition system for fabricated semiconductor wafers
Author/Authors :
Ooi، نويسنده , , Melanie Po-Leen and Sok، نويسنده , , Hong Kuan and Kuang، نويسنده , , Ye Chow and Demidenko، نويسنده , , Serge and Chan، نويسنده , , Chris، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2013
Pages :
15
From page :
1029
To page :
1043
Abstract :
The International Technology Roadmap for Semiconductors (ITRS) identifies production test data as an essential element in improving design and technology in the manufacturing process feedback loop. One of the observations made from the high-volume production test data is that dies that fail due to a systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying and categorising such clusters is a crucial step towards manufacturing yield improvement and implementation of real-time statistical process control. Addressing the semiconductor industry’s needs, this research proposes an automatic defect cluster recognition system for semiconductor wafers that achieves up to 95% accuracy (depending on the product type).
Keywords :
feature extraction , Recognition , Semiconductor wafer fabrication , Defect cluster classification
Journal title :
Engineering Applications of Artificial Intelligence
Serial Year :
2013
Journal title :
Engineering Applications of Artificial Intelligence
Record number :
2125877
Link To Document :
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