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We discuss the methodology of physical resist model calibration for a rigorous lithography simulator under various aspects and assess the resulting predictive accuracy. The study is performed on an extensive optical proximity correction ͑OPC͒ dataset, which includes several thousands of critical dimensions ͑CDs͒ values obtained with immersion lithography for the 45-nm half-pitch technology node. We address practical aspects such as speed of calibration versus size of calibration dataset, and the role of pattern selection for calibration. In particular, we show that a small subset of the dataset is sufficient to provide accurate calibration results. However, the overall predictive power can strongly be enhanced if a few critical patterns are additionally included into the calibration dataset. Also, we demonstrate a significant impact of the illumination source shape ͑measured versus nominal top hat͒ on the resulting model quality. Most importantly, it is shown that calibrated resist models based on a 3-D ͑topographic͒ mask description perform better than resist models based on a 2-D ͑Kirchhoff͒ mask approximation. Also, we show that a resist model calibrated with one-dimensional ͑lines and spaces͒ structures only can successfully predict the printing behavior of twodimensional patterns ͑end-of-line structures͒.

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We report on a comparison between a fullphysical resist model that was calibrated to experimental line/space ͑L/S͒ critical dimension ͑CD͒ data under the flat-mask ͑also called "thin-mask" or "Kirchhoff"͒ approximation with the model obtained when using a mask 3-D calculation engine ͑i.e., one that takes into account the masktopography effects͒. Both models were tested by evaluating their prediction of the CDs of a large group of 1-D and 2-D structures. We found a clear correlation between the measured-predicted CD difference and the magnitude of the mask 3-D CD effect, and show that the resist model calibrated with a mask 3-D calculation engine clearly offers a better CD predictability for certain types of structures.Calibration of full-physical resist models 1-4 has a long record of use and is constantly being refined to offer better predictability to rigorous simulators, such as Sentaurus Lithography™ of Synopsys or Prolith™ of KLA-Tencor. We used a large experimental critical dimension ͑CD͒ data set, which was generated for the purpose of optical proximity correction ͑OPC͒-model building, to assess the importance of mask 3-D ͑or mask-topography͒ effects in resist-model calibration. The CD data was measured with a top-down Hitachi H9380 scanning electron microscope from a 120 nm TOK TArF-Pi6-001-ME resist on 95 nm ARC29SR Barc on Si wafer exposed with an NA= 1.20 ASML XT:1700i immersion scanner, using cQuad20 outer / inner = 0.96/ 0.60 XY-polarized illumination. The experimental CD data consisted of two types. The first type consisted of Bossung ͑i.e., through-dose and -focus͒ data for 30 L/S structures, with pitches between 100 and 400 nm. The resist models were essentially calibrated using ͑part of͒ these L/S Bossungs only. The second CD-data group ͑ϳ5000 different structures͒ was measured at a single dose-focus setting only and was used for verifying the resist models. It consisted of more 1-D-type data ͑L/S structures with and without SRAFs, isolated lines and trenches and line and trench doublets and triplets͒, end-of-line ͑EOL͒ gap-CD data and of CDs measured from more complicated 2-D structures ͑which we shall call the Generic 2-D or G2D structures͒. Most of the structures in this data set are located on the mask in a locally clear-field area, but others are located in a locally dark-field area. The resist-model quality is then quantified by first calculating the measured-predicted CD difference, ⌬CDϵ CDគ measured-CDគsimulated, for all individual verification structures. In view of the very large number of structures in the verification set ͑ Ͼ 5000͒, we reduced this ⌬CD data set by calculating the mean value ͓Mean͑⌬CD͔͒ and the standard deviation ͓StDev͑⌬CD͔͒ for a number of-somewhat arbitrarily chosen-subsets of this verification-structure group.In Fig. 1, we represent such a verification result, by plotting these Mean͑⌬CD͒ values for each of the selected structure subsets ͑the labels we use for these subsets are explained in the caption of Fig. 1͒. The error bars in Fig. 1͑b͒ actually stand for the ...

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