Apparatus and method for grouping image patterns to determine wafer behavior in a patterning process

Abstract

Grouping image patterns to determine wafer behavior in a patterning process with a trained machine learning model is described. The described operations include converting, based on the trained machine learning model, one or more patterning process images including the image patterns into feature vectors. The feature vectors correspond to the image patterns. The described operations include grouping, based on the trained machine learning model, feature vectors with features indicative of image patterns that cause matching wafer and/or wafer defect behavior in the patterning process. The one or more patterning process images include aerial images, resist images, and/or other images. The grouped feature vectors may be used to: detect potential patterning defects on a wafer during a lithography manufacturability check as part of optical proximity correction, adjust a mask layout design, and/or generate a gauge line/defect candidate list, among other uses.

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