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6 March 2009 Improved model predictability by machine data in computational lithography and application to laser bandwidth tuning
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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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stefan Hunsche, Qian Zhao, Xu Xie, Robert Socha, Hua-Yu Liu, Peter Nikolsky, Anthony Ngai, Paul van Adrichem, Michael Crouse, and Ivan Lalovic "Improved model predictability by machine data in computational lithography and application to laser bandwidth tuning", Proc. SPIE 7274, Optical Microlithography XXII, 727405 (6 March 2009); https://doi.org/10.1117/12.814644
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