Note on the Use of Principal Component Analysis (PCA) and Clustering for the Analysis of Wafer Level ATPG data
Keyword(s):
Abstract This article explores the use of principal component analysis (PCA) and hierarchical clustering in the analysis of wafer level automatic test pattern generation (ATPG) failure data. The principle of commonality is extended by utilizing hierarchical clustering to collect die that are more similar to one another in their manner of failure than to others. Similarity is established by PCA of the patterns that the die in a wafer fail. Results demonstrated that PCA analysis and clustering are useful tools for dimensionality reduction and commonality analysis of wafer level ATPG data. The utility of PCA analysis and clustering in the extraction of die for physical failure analysis is also illustrated.
2020 ◽
Vol 790
◽
pp. 012085
2017 ◽
Vol 38
(2)
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pp. 108-128