Variance reduction and outlier identification for I [subscript DDQ] testing of integrated chips using principal component analysis [electronic resource] /
Integrated circuits manufactured in current technology consist of millions of transistors with dimensions shrinking into the nanometer range. These small transistors have quiescent (leakage) currents that are increasingly sensitive to process variations, which have increased the variation in good-chip quiescent current and consequently reduced the effectiveness of I [subscript DDQ] testing. This research proposes the use of a multivariate statistical technique known as principal component analysis for the purpose of variance reduction. Outlier analysis is applied to the reduced leakage current values as well as the good chip leakage current estimate, to identify defective chips. The proposed idea is evaluated using I [subscript DDQ] values from multiple wafers of an industrial chip fabricated in 130nm technology. It is shown that the proposed method achieves significant variance reduction and identifies many outliers that escape identification by other established techniques. For example, it identifies many of the absolute outliers in bad neighborhoods, which are not detected by Nearest Neighbor Residual and Nearest Current Ratio. It also identifies many of the spatial outliers that pass when using Current Ratio. The proposed method also identifies both active and passive defects.
School:Texas A&M International University
School Location:USA - Texas
Source Type:Master's Thesis
Keywords:major computer engineering variance reduction iddq testing pca
Date of Publication: