Enhanced critical feature representation for fuzzy-matching for lithography hotspot detection

Mohamed M. Elshabrawy; Amr G. Wassal

doi:10.1117/12.2279538

16 October 2017 Enhanced critical feature representation for fuzzy-matching for lithography hotspot detection

Mohamed M. Elshabrawy, Amr G. Wassal

Proceedings Volume 10451, Photomask Technology 2017; 104510T (2017) https://doi.org/10.1117/12.2279538
Event: SPIE Photomask Technology and EUV Lithography, 2017, Monterey, California, United States

Abstract

The detection of problematic low-fidelity lithographic design patterns in the physical verification phase is considered to be one of the greatest challenges and important techniques nowadays in the manufacturing process. There are lots of contributions to detect the lithographic hotspots, which mostly rely on using machine learning (ML) and/or pattern matching (PM) approaches. The fuzzy PM was presented due to the limitations in ML approach, where inappropriate layout feature representation impacts the number of false alarms, and the PM approach, which lacks prediction or identifying two similar patterns. However, the tricky step in fuzzy PM techniques is to define a similarity measurement between two patterns. This paper proposes a new feature representation for fuzzy matching for lithography hotspot detection. The technique enhances the modified-transitive-closure-graph (MTCG) by adding specific do-not-care (DC) regions to filter out unwanted polygons. This technique is capable of reaching 88% success rates, by 10% increase in success rates compared to the conventional MTCG, with no impact on total run-time. Building on the uniqueness of MTCGs for any tiled pattern, a new similaritydetection technique is also introduced that detects hotspots of similar shapes with acceptable defined tolerance. The proposed technique, besides MTCG, is able to reach 97.044% success rate with only 1.0287% increase in total run-time.

Citation Download Citation

Mohamed M. Elshabrawy and Amr G. Wassal "Enhanced critical feature representation for fuzzy-matching for lithography hotspot detection", Proc. SPIE 10451, Photomask Technology 2017, 104510T (16 October 2017); https://doi.org/10.1117/12.2279538

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