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Proceedings Paper

Calibration of physical resist models: methods, usability, and predictive power
Author(s): Ulrich K. Klostermann; Thomas Mülders; Denis Ponomarenco; Thomas Schmöller; Jeroen Van de Kerkhove; Peter De Bisschop
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Paper Abstract

We discuss the methodology of resist model calibration under various aspects and assess the resulting predictive accuracy. The study is performed on an extensive OPC data set which includes several thousands of CD values obtained with immersion lithography for the 45 nm technology node. We address practical aspects such as speed of calibration vs. size of calibration data set and the role of pattern selection for calibration. In particular, we show that a small subset of the data set 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 data set. Besides, we demonstrate a significant impact of the illumination source shape (measured vs. nominal top hat) on the resulting model quality. Most importantly, it will be shown that calibrated resist models based on a 3D (topographic) mask description perform better than resist models based on a 2D (Kirchhoff) mask approximation. Also, we show that a resist model calibrated with one-dimensional (lines & spaces) structures only can successfully predict the printing behavior of two-dimensional patterns (end-of-line structures).

Paper Details

Date Published: 1 April 2009
PDF: 12 pages
Proc. SPIE 7273, Advances in Resist Materials and Processing Technology XXVI, 727342 (1 April 2009); doi: 10.1117/12.814216
Show Author Affiliations
Ulrich K. Klostermann, Synopsys GmbH (Germany)
Thomas Mülders, Synopsys GmbH (Germany)
Denis Ponomarenco, Synopsys GmbH (Germany)
Thomas Schmöller, Synopsys GmbH (Germany)
Jeroen Van de Kerkhove, IMEC (Belgium)
Peter De Bisschop, IMEC (Belgium)


Published in SPIE Proceedings Vol. 7273:
Advances in Resist Materials and Processing Technology XXVI
Clifford L. Henderson, Editor(s)

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