SEPARATION DISTANCE BETWEEN FEATURE VECTORS FOR SEMI-SUPERVISED HOTSPOT DETECTION AND CLASSIFICATION

摘要

Systems and methods for semi-supervised hotspot detection and classification are disclosed. Hotspots comprise layout pattern that induce printability issues in the lithography process. To detect hotspots, one feature vector, such as an n-dimensional feature vector, is compared with other feature vector(s). The comparison between feature vectors may comprise determining a distance, such as a Euclidian distance, in order to determine closeness between the feature vectors. For example, a training dataset, that includes known hotspots and known non-hotspots, is used in order to determine threshold(s). In particular, for one, some, or all of the known hotspots in the training dataset, a distance to a closest known hotspot and a closest known non-hotspot may be calculated to determine the threshold(s). In turn, a layout under examination, which includes indeterminate spots, may be analyzed using the known hotspots in the training dataset and the threshold(s) to identify the indeterminate spots as potential hotspots.