Wavelet transform based wafer defect map pattern recognition system in semiconductor manufacturing

In recent years, the semiconductor industry has been a high-growth business. Yield improvement becomes more and more important for semiconductor manufacturing process and it's the important element to ensure the profitability. Defect spatial patterns impede yield and it reveal the root causes of the occurred defects. This research developed a defect spatial pattern recognition methodology. In this research, defect spatial patterns are classified into seven categories, Curve Type, Line Type, Local Type, Ring Type, Radial Type, and Repeat Type and Die Edge Type. Original defects are filtered to eliminate random defects. With random defect removed, a clustering method is designed to cluster systematic defects. Clustered defects are then analyzed with minimum rectangle. A feature extraction procedure based on wavelet transform is developed to extract features that represent different defect patterns. By setting feature vectors into feature space and applying hierarchical agglomerative algorithm to clustering, it is expected to reach the goal of description and classification. The presented methodology is verified with real industrial data from a famous semiconductor company. The experimental results show the presented methodology is able to recognize defect patterns with recognition accuracy of 95%.