Journal
IEEE ACCESS
Volume 9, Issue -, Pages 78873-78882Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3084221
Keywords
Pattern analysis; Clustering algorithms; Shape; Licenses; Probes; Materials handling; Manufacturing processes; Wafer map analysis; defect patterns; density-based clustering; spatial randomness test
Categories
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2019R1A6A1A03032119]
- National Research and Development Program through the NRF - Ministry of Science and ICT [NRF-2020M3H2A1078119]
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The study proposes a defect pattern analysis method based on density-based clustering (DBC), which includes statistical testing for detecting abnormal defects in wafer maps and clustering of defect patterns, both steps performed simultaneously using core points from DBC. The proposed method is shown to accurately identify spatial dependence among defects with much less computational effort compared to existing methods.
For a yield enhancement in semiconductor manufacturing, it is necessary to analyze wafer maps since they contain information gathered during the manufacturing such as test results of each chip. Especially, spatial patterns of defective chips, e.g. zone, scratch, ring patterns, etc. presented on a wafer map provide valuable information on the potential causes of malfunctions in a fabrication process. Numerous automatic analysis methods have been developed for identifying such defect patterns. We propose a defect pattern analysis method based on density-based clustering (DBC), which consists of two steps: conducting a statistical test to detect wafer maps that contain abnormal defects and clustering the defect patterns. Specifically, we develop a new statistic based on the core points from DBC for the spatial randomness test, which requires much fewer examinations to identify abnormal wafer maps than the existing joint-count based statistics. With those core points, clustering of abnormal defects can be coherently performed in the subsequent clustering step. The main advantage of our method over previous automatic detection methods is that it performs both steps simultaneously based on the core points from DBC. The proposed method is evaluated on simulated and real wafer map datasets. Experimental results show that the proposed method identifies spatial dependence among defects as accurate as the existing methods, but with much less computational effort.
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