Siemens Industry Software Gmbh

Le Hong

Yuansheng Ma

Shumay Dou Shang

Lianghong Yin

Junjiang Lei

Fan Jiang

Rui Wu

Various aspects of the present disclosed technology relate to techniques for inverse-lithography-technology-based optical proximity correction. A layout design is received. A machine learning-based clustering process is then performed to separate layout features in the layout design into groups of layout features. For layout features in each of the groups of layout features, preliminary corrections are determined. The determination may be based on inverse lithography technology. The preliminary corrections are applied to the layout design to generate a pre-processed layout design. An inverse lithography technology process is performed on the pre-processed layout design to generate a processed layout design. Masks can be manufactured based on the processed layout design.

Optical proximity correction based on combining inverse lithography technology with pattern classification

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.

Machine learning-based clustering for curvilinear layout designs

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Benicia, CA, United States of America

Le Hong