[논문]Similarity Search on Wafer Bin Map Through Nonparametric and Hierarchical Clustering

Lee, Jea Hoon; Moon, Il-Chul; Oh, Rosy

doi:10.1109/tsm.2021.3102679

[해외논문] Similarity Search on Wafer Bin Map Through Nonparametric and Hierarchical Clustering

IEEE transactions on semiconductor manufacturing : a publication of the IEEE Components, Hybrids, and Manufacturing Technology Society, the IEEE Electron Devices Society, the IEEE Reliability Society,, v.34 no.4, 2021년, pp.464 - 474

Lee, Jea Hoon (Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea) , Moon, Il-Chul (Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea) , Oh, Rosy (Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea)

Abstract ▼ AI-Helper

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.

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DOI : 10.1109/TSM.2021.3102679
IEEE : 저널 > 논문

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