A Unified Defect Pattern Analysis of Wafer Maps Using Density-Based Clustering

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.

Automated summary

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.