Abstract: Various aspects of the present disclosed technology relate to techniques for classifying layout patterns. First, a set of density feature vectors for a set of layout regions in the layout design are extracted using a set of rings. Each component of a density feature vector in the set of density feature vectors corresponds to a ring in the set of rings. The set of rings do not overlap with each other and cover a whole area of a circle when being placed together. Next, a machine learning-based clustering process is performed to separate layout features in the set of layout regions into clusters of layout features based on the set of density feature vectors. Each of the clusters of layout features may be further divided into subclusters based on one or more properties.

Abstract: Various aspects of the present disclosed technology relate to techniques for classifying layout patterns. First, a set of density feature vectors for a set of layout regions in the layout design are extracted using a set of rings. Each component of a density feature vector in the set of density feature vectors corresponds to a ring in the set of rings. The set of rings do not overlap with each other and cover a whole area of a circle when being placed together. Next, a machine learning-based clustering process is performed to separate layout features in the set of layout regions into clusters of layout features based on the set of density feature vectors. Each of the clusters of layout features may be further divided into subclusters based on one or more properties.

Abstract: Systems and methods for calculating a printed area metric indicative of stochastic variations of the lithographic process are disclosed. Lithography is a process that uses light to transfer a geometric pattern from a photomask, based on a layout design, to a resist on a substrate. The lithographic process is subject to random stochastic phenomena, with the resulting stochastic randomness potentially becoming a major challenge. To characterize the stochastic phenomena, a printed area metric may be generated analytically (rather than via simulations) and comprise one or more defined moments for a printed area distribution associated with the printed area that are indicative of one or more aspects associated with printing. For example, the printed area metric may be indicative of the likelihood of printing within the printed area or the variance of printing within the printed area due to stochastic randomness in one or both of exposure or resist process.

Abstract: Systems and methods for calculating a printed area metric indicative of stochastic variations of the lithographic process are disclosed. Lithography is a process that uses light to transfer a geometric pattern from a photomask, based on a layout design, to a resist on a substrate. The lithographic process is subject to random stochastic phenomena, with the resulting stochastic randomness potentially becoming a major challenge. To characterize the stochastic phenomena, a printed area metric may be generated analytically (rather than via simulations) and comprise one or more defined moments for a printed area distribution associated with the printed area that are indicative of one or more aspects associated with printing. For example, the printed area metric may be indicative of the likelihood of printing within the printed area or the variance of printing within the printed area due to stochastic randomness in one or both of exposure or resist process.