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http://dx.doi.org/10.5351/KJAS.2022.35.3.407

Wafer bin map failure pattern recognition using hierarchical clustering  

Jeong, Joowon (Department of Statistics, Korea University)
Jung, Yoonsuh (Department of Statistics, Korea University)
Publication Information
The Korean Journal of Applied Statistics / v.35, no.3, 2022 , pp. 407-419 More about this Journal
Abstract
The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.
Keywords
failure pattern classification; failure pattern detection; hierarchical clustering; wafer bin map;
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