Layout Hotspot Pattern Clustering Using a Density-based Approach

Since the number of hotspot patterns detected on a layout using machine learning technique is very large, it takes designers a lot of time to classify these hotspot patterns for subsequent modification. These hotspot patterns are diverse and complex in shape. Therefore, we propose a density-based hotspot pattern clustering approach to classify these hotspot patterns into groups, which extracts the density feature of hotspot patterns while considering the shifted and distorted polygons on hotspot patterns. Experimental results show that our approach can classify the hotspot patterns more efficiently than SIFT method with similar results in each group.