Similarity Search on Wafer Bin Map Through Nonparametric and Hierarchical Clustering

• Published in: IEEE transactions on semiconductor manufacturing Vol. 34; no. 4; pp. 464 - 474
• Main Authors: Lee, Jea Hoon, Moon, Il-Chul, Oh, Rosy
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayes methods; Cluster analysis; Clustering; Clustering methods; defect pattern; Dirichlet problem; Failure analysis; Feature extraction; Gaussian process; Integrated circuits; Manufacturing; nonparametric Bayesian clustering; Nonparametric statistics; Probabilistic models; Root cause analysis; Semiconductor device measurement; Similarity; Similarity ranking; wafer bin map
• ISSN: 0894-6507; 1558-2345
• DOI: 10.1109/TSM.2021.3102679

Searching for and comparing similar wafer maps can provide crucial information for root cause analysis in the manufacturing process of integrated circuits. Owing to the high dimensionality and complexity of defect patterns, comparison of similar maps in their entirety is inefficient. This paper proposes an automated similarity ranking system with a novel feature set as a reduced representation of wafer maps. To detect systematic failure patterns across wafer maps, we use nonparametric Bayesian clustering based on the Dirichlet process Gaussian mixture model, and hierarchical clustering based on the symmetric Kullback-Leibler divergence. The proposed features are efficient because they require minimal computation and storage; furthermore, they allow for highly discriminative rankings of similar failure patterns. Thus they are suitable for large-scale analysis of wafer maps. The proposed method is experimentally verified using a real wafer map dataset from a semiconductor manufacturing company, and a subset of WM-811K.