DocumentCode :
262778
Title :
Spatio-temporal wafer-level correlation modeling with progressive sampling: A pathway to HVM yield estimation
Author :
Ahmadi, Ali ; Ke Huang ; Natarajan, Suriyaprakash ; Carulli, John M. ; Makris, Yiorgos
Author_Institution :
Dept. of Electr. Eng., Univ. of Texas at Dallas, Richardson, TX, USA
fYear :
2014
fDate :
20-23 Oct. 2014
Firstpage :
1
Lastpage :
10
Abstract :
Wafer-level spatial correlation modeling of probetest measurements has been explored in the past as an avenue to test cost and test time reduction. In this work, we first improve the accuracy of a popular Gaussian process-based wafer-level spatial correlation method through two key enhancements: (i) confidence estimation-based progressive sampling, and, (ii) inclusion of spatio-temporal features for inter-wafer trend learning. We then explore a new application of the enhanced correlation modeling method in estimating High Volume Manufacturing (HVM) yield from a small set of early wafers and we demonstrate its effectiveness on a large set of actual industrial test data.
Keywords :
Gaussian processes; integrated circuit modelling; integrated circuit testing; integrated circuit yield; Gaussian process-based wafer-level spatial correlation; HVM yield estimation; confidence estimation-based progressive sampling; high volume manufacturing yield; inter-wafer trend learning; probe-test measurements; spatio-temporal features; spatio-temporal wafer-level correlation modeling; Accuracy; Correlation; Gaussian processes; Kernel; Predictive models; Semiconductor device modeling; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Test Conference (ITC), 2014 IEEE International
Conference_Location :
Seattle, WA
Type :
conf
DOI :
10.1109/TEST.2014.7035325
Filename :
7035325
Link To Document :
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