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Proceedings Paper

Process model quality assessment by sensitivity analysis
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Paper Abstract

An accurate process model is the linchpin of model-based Optical Proximity Correction (OPC) and Resolution Enhancement Technique (RET) synthesis. The accuracy of the resulting mask layout can be no better than that of the model. Relatively good, first-principle mathematical models exist for some process steps, such as aerial image formation, but resulting silicon is a combination of many effects, including those less well understood. Accuracy can be assured only with models anchored to observed phenomena. Process models are usually a combination of first principle elements and phenomenological components with the “right” degrees to freedom to fit the overall process. The key challenge in generating accurate models is to capture all process behavior over all conditions with a minimum number of empirical measurements. This means that models must extrapolate accurately from the specifics measured, and should be largely immune to empirical measurement noise. In this paper we describe a methodology in which to test model performance with respect to these criteria.

Paper Details

Date Published: 28 June 2005
PDF: 7 pages
Proc. SPIE 5853, Photomask and Next-Generation Lithography Mask Technology XII, (28 June 2005); doi: 10.1117/12.617197
Show Author Affiliations
Qiliang Yan, Synopsys Inc. (United States)
Lawrence S. Melvin, Synopsys Inc. (United States)
James P. Shiely, Synopsys Inc. (United States)


Published in SPIE Proceedings Vol. 5853:
Photomask and Next-Generation Lithography Mask Technology XII
Masanori Komuro, Editor(s)

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