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Proceedings Paper

Improved model predictability by machine data in computational lithography and application to laser bandwidth tuning
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Paper Abstract

Computational lithography (CL) is becoming more and more of a fundamental enabler of advanced semiconductor processing technology, and new requirements for CL models are arising from new applications such as model-based process tuning. In this paper we study the impact of realistic machine parameters that can be incorporated in a modern CL model, and provide an experimental assessment of model improvements with respect to prediction of scanner tuning effects. The data demonstrates improved model accuracy and prediction by inclusion of scanner-type specific modeling capabilities and machine data in the CL model building process. In addition to scanner effects, we study laser bandwidth tuning effects and the accuracy of corresponding model predictions by comparison against experimental data. The data demonstrate that the models predict well wafer CD variations resulting from laser BW tuning. We also find that using realistic spectral density distribution of the laser can provide more accurate results than the commonly assumed modified Lorentzian line shape.

Paper Details

Date Published: 6 March 2009
PDF: 11 pages
Proc. SPIE 7274, Optical Microlithography XXII, 727405 (6 March 2009); doi: 10.1117/12.814644
Show Author Affiliations
Stefan Hunsche, Brion Technologies (United States)
Qian Zhao, Brion Technologies (United States)
Xu Xie, Brion Technologies (United States)
Robert Socha, ASML (United States)
Hua-Yu Liu, Brion Technologies (United States)
Peter Nikolsky, ASML Netherlands B.V. (Netherlands)
Anthony Ngai, ASML Netherlands B.V. (Netherlands)
Paul van Adrichem, ASML Netherlands B.V. (Netherlands)
Michael Crouse, ASML Corp. (United States)
Ivan Lalovic, Cymer, Inc. (United States)


Published in SPIE Proceedings Vol. 7274:
Optical Microlithography XXII
Harry J. Levinson; Mircea V. Dusa, Editor(s)

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