Synopsys, Inc.

Hui-Ru Jiang

Charles C Chiang

Yen-Ting Yu

Subarnarekha Sinha

Geng-He Lin

Yumin Zhang

Ya-Chung Chan

Yen Ting Yu

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

A range-pattern-matching-type DRC-based process hotspot detection is provided that considers edge tolerances and incomplete specification ('don't care') regions in foundry-provided hotspot patterns. First, all possible topological patterns are enumerated for the foundry-provided hotspot pattern. Next, critical topological features are extracted from each pattern topology and converted to critical design rules using Modified Transitive Closure Graphs (MTCGs). Third, the extracted critical design rules are arranged in an order that facilitates searching space reduction techniques, and then the DRC process is sequentially repeated on a user's entire layout pattern for each critical design rule in a first group, then searching space reduction is performed to generate a reduced layout pattern, and then DRC process is performed for all remaining critical design rules using the reduced layout pattern. Candidate locations are then identified using the DRC results, and then the true hotspot locations are confirmed using longest common subsequence and linear scan techniques.

DRC-based hotspot detection considering edge tolerance and incomplete specification

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.

Accurate process hotspot detection using critical design rule extraction

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Taipei, Taiwan

Hui-Ru Jiang