In advanced technology nodes, lithography hotspot detection has become one of the most significant issues in design for manufacturability. Recently, machine learning based lithography hotspot detection has been widely investigated, but it has trade-off between detection accuracy and false alarm. To apply machine learning based technique to the physical verification phase, designers require minimizing undetected hotspots to avoid yield degradation. They also need a ranking of similar known patterns with a detected hotspot to prioritize layout pattern to be corrected. To achieve high detection accuracy and to prioritize detected hotspots, we propose a novel lithography hotspot detection method using Delaunay triangulation and graph kernel based machine learning. Delaunay triangulation extracts features of hotspot patterns where polygons locate irregularly and closely one another, and graph kernel expresses inner structure of graphs. Additionally, our method provides similarity between two patterns and creates a list of similar training patterns with a detected hotspot. Experiments results on ICCAD 2012 benchmarks show that our method achieves high accuracy with allowable range of false alarm. We also show the ranking of the similar known patterns with a detected hotspot.
Izumi Nitta, Yuzi Kanazawa, Tsutomu Ishida, and Koji Banno, "A fuzzy pattern matching method based on graph kernel for lithography hotspot detection," Proc. SPIE 10148, Design-Process-Technology Co-optimization for Manufacturability XI, 101480U (Presented at SPIE Advanced Lithography: March 03, 2017; Published: 28 March 2017); https://doi.org/10.1117/12.2257654.
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