Classification of lines, spaces, and edges of resist patterns in scanning electron microscopy images using unsupervised machine learning

As key steps of lithography, the development of resist materials and the exploration of new materials are important to meet market demands from the semiconductor industry. During the development, resist materials are usually evaluated by the information extracted from their scanning electron microscopy (SEM) images. The information extracted from SEM images is not always accurate owing to technical limitation. Accurate information extraction is also useful for the prediction of an etched substrate pattern. In this paper, we reported a strategy to classify the image pixels of line-and-space resist patterns into line, space, and edge classes, using unsupervised machine learning. Brightness and coordination information was integrated into the classification method. The high reliability in classification was demonstrated by hierarchical clustering based on its information integrating ability. Among all the methods of hierarchical clustering examined, the centroid method was the most accurate strategy for extracting information from a single SEM image.