Improved segmentation of semiconductor defects using area sieves

This paper aims at investigating a novel non-referential solution to the problem of defect detection on semiconductor wafer-die images. The suggested solution focuses on segmenting defects from the images using wavelet transformation and morphology-related properties of the associated wavelet coefficients. More specifically, a novel methodology is investigated for segmenting defects by applying an area sieves technique to innovative multidimensional wavelet-based features. These features are extracted from the original defective image using the non-reference K-Level 2-D DWT (Discrete Wavelet Transform). The results of the proposed methodology are illustrated in defective die images where the defective areas are segmented with higher accuracy than the one obtained by applying other reference-based feature extraction methodologies. The first uses all the wavelet coefficients derived from the K-Level 2-D DWT, while the second one uses area sieves to segment the defective regions. Both methods involve in the same classification stage as the proposed feature extraction approach. The promising results obtained outline the importance of judicious selection and processing of 2-D DWT wavelet coefficients for industrial pattern recognition applications.