8 November 2012 Hotspot classification based on higher-order local autocorrelation
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Abstract
Hotspot classification is an important step of hotspot management. Under possible center-shifting condition, conventional hotspot classification by calculating pattern similarity through overlaying two hotspot patterns directly is not effective. This paper proposes a hotspot classification method based on higher-order local autocorrelation (HLAC). Firstly, we extract the features of the hotspot patterns using HLAC method. Secondly, the principal component analysis (PCA) is performed on the features for dimension reduction. Thirdly, the simplified low dimensional vector features of the hotspot patterns are used in the pre-clustering step. Finally, detailed clustering using pattern similarity calculated by discrete 2-d correlation is carried out. Because the HLAC based features are shifting-invariant, the center-shifting problem caused by the defect location inaccuracy can be overcome during the pre-clustering process. Experiment results show that the proposed method can classify hotspots under center-shifting condition effectively and speed up the classification process greatly.
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Bin Lin, Bin Lin, Zheng Shi, Zheng Shi, Ye Chen, Ye Chen, "Hotspot classification based on higher-order local autocorrelation", Proc. SPIE 8522, Photomask Technology 2012, 85222O (8 November 2012); doi: 10.1117/12.964384; https://doi.org/10.1117/12.964384
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