DocumentCode :
1444121
Title :
Determining a Failure Root Cause Distribution From a Population of Layout-Aware Scan Diagnosis Results
Author :
Benware, Brady ; Schuermyer, Chris ; Sharma, Manish ; Herrmann, Thomas
Author_Institution :
Mentor Graphics Corp., Wilsonville, OR, USA
Volume :
29
Issue :
1
fYear :
2012
Firstpage :
8
Lastpage :
18
Abstract :
The yield of an integrated circuit (IC) is well known to be a critical factor in the success of an IC in the market place. Achieving high stable yields helps ensure that the product is profitable and meets quality and reliability objectives. When a new manufacturing process is introduced, or a new product is introduced on a mature manufacturing process, yields will tend to be significantly lower than acceptable. The ability to meet profitability and quality objectives, and perhaps more importantly, time-to-market and time-to-volume objectives depend greatly on the rate at which these low yields can be ramped up. While the yield ramp depends on both the yield learning and yield enhancement cycle times, this work focuses on significantly increasing the value of test data and the yield learning rate.
Keywords :
Bayes methods; expectation-maximisation algorithm; failure analysis; fault diagnosis; inference mechanisms; integrated circuit layout; integrated circuit reliability; integrated circuit testing; integrated circuit yield; learning (artificial intelligence); logic testing; production engineering computing; profitability; quality control; time to market; Bayes net model; IC yield; expectation-maximization principle; failure root cause distribution; integrated circuit yield; layout-aware scan diagnosis result population; learning algorithm; learning process; logic-test-failing die; manufacturing process; product profitability; quality objectives; reliability objectives; root cause inference; test data; time-to-market objectives; time-to-volume objectives; yield enhancement cycle time; yield learning rate; yield ramp; Bayesian methods; Fault diagnosis; Feature extraction; Learning systems; Machine learning; Random variables; ATPG; Bayesian Networks; Machine Learning; Scan Diagnosis; Yield Learning;
fLanguage :
English
Journal_Title :
Design & Test of Computers, IEEE
Publisher :
ieee
ISSN :
0740-7475
Type :
jour
DOI :
10.1109/MDT.2011.2178386
Filename :
6148303
Link To Document :
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