Hybrid hotspot detection using regression model and lithography simulation

Taiki Kimura; Tetsuaki Matsunawa; Shigeki Nojima; David Z. Pan

doi:10.1117/12.2219318

22 March 2016 Hybrid hotspot detection using regression model and lithography simulation

Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima, David Z. Pan

Proceedings Volume 9781, Design-Process-Technology Co-optimization for Manufacturability X; 97810C (2016) https://doi.org/10.1117/12.2219318
Event: SPIE Advanced Lithography, 2016, San Jose, California, United States

Abstract

As minimum feature sizes shrink, unexpected hotspots appear on wafers. Therefore, it is important to detect and fix these hotspots at design stage to reduce development time and manufacturing cost. Currently, as the most accurate approach, lithography simulation is widely used to detect such hotspots. However, it is known to be time-consuming. This paper proposes a novel aerial image synthesizing method using regression and minimum lithography simulation for only hotspot detection. Experimental results show hotspot detection on the proposed method is equivalent compared with the results on the conventional hotspot detection method which uses only lithography simulation with much less computational cost.

Citation Download Citation

Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima, and David Z. Pan "Hybrid hotspot detection using regression model and lithography simulation", Proc. SPIE 9781, Design-Process-Technology Co-optimization for Manufacturability X, 97810C (22 March 2016); https://doi.org/10.1117/12.2219318

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