Synopsys, Inc.

Yen-Ting Yu

Charles C Chiang

Hui-Ru Jiang

Subarnarekha Sinha

Geng-He Lin

Ya-Chung Chan

A hotspot detection system that classifies a set of hotspot training data into a plurality of hotspot clusters according to their topologies, where the hotspot clusters are associated with different hotspot topologies, and classifies a set of non-hotspot training data into a plurality of non-hotspot clusters according to their topologies, where the non-hotspot clusters are associated with different topologies. The system extracts topological and non-topological critical features from the hotspot clusters and centroids of the non-hotspot clusters. The system also creates a plurality of kernels configured to identify hotspots, where each kernel is constructed using the extracted critical features of the non-hotspot clusters and the extracted critical features from one of the hotspot clusters, and each kernel is configured to identify hotspot topologies different from hotspot topologies that the other kernels are configured to identify.

Lithographic hotspot detection using multiple machine learning kernels

An accurate process hotspot detection technique based on DRC is provided. In this technique, critical DRC rules can be extracted from a pattern. This extraction can include generating horizontal tiles and vertical tiles in the pattern, and adding directed edges to indicate relations between adjacent tiles in the pattern. Rule rectangles, which can also be generated during the critical DRC rule extraction, describe polygon placement in the pattern with a minimal number of critical DRC rules. The extracted DRC rules can be included in a DRC runset file. DRC can be performed with the DRC runset file on a layout. The DRC results can be filtered using the rule rectangles to identify potential hotspots and to verify actual hotspots.

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Taipei, Taiwan

Yen-Ting Yu