Scanning electron microscopy (SEM) image is an indispensable device in inspection of photoresist and etched Si patterns. On the other hand, extreme ultraviolet (EUV) radiation offers high resolution in lithography fabrication. Owing to the application of EUV, the feature size of photomask produced by electron beam (EB) lithography is reduced as well. The reduction of feature size made the information extraction from SEM image difficult due to the technical limitation. This proceeding reports a strategy to extract the information of line-and-space pattern. The main step classification of line, space and edge class was realized by unsupervised machine learning – hierarchical clustering. Hierarchical clustering can integrate the brightness and coordination information in classification, and thus the classification ability of it is better than image thresholding. Furthermore, the classification result was used in prediction for etching pattern. The precision and recall of prediction were verified by confusion matrix.
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