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Proceedings Paper

Application of Principal Component Analysis to EUV multilayer defect printing
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Paper Abstract

This paper proposes a new method for the characterization of multilayer defects on EUV masks. To reconstruct the defect geometry parameters from the intensity and phase of a defect, the Principal Component Analysis (PCA) is employed to parametrize the intensity and phase distributions into principal component coefficients. In order to construct the base functions of PCA, a combination of a reference multilayer defect and appropriate pupil filters is introduced to obtain the designed sets of intensity and phase distributions. Finally, an Artificial Neural Network (ANN) is applied to correlate the principal component coefficients of the intensity and the phase of the defect with the defect geometry parameters and to reconstruct the unknown defect geometry parameters.

Paper Details

Date Published: 23 September 2015
PDF: 11 pages
Proc. SPIE 9630, Optical Systems Design 2015: Computational Optics, 96300Y (23 September 2015); doi: 10.1117/12.2190784
Show Author Affiliations
Dongbo Xu, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Fraunhofer Institute for Integrated Systems and Device Technology (Germany)
Peter Evanschitzky, Fraunhofer Institute for Integrated Systems and Device Technology (Germany)
Andreas Erdmann, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Fraunhofer Institute for Integrated Systems and Device Technology (Germany)


Published in SPIE Proceedings Vol. 9630:
Optical Systems Design 2015: Computational Optics
Daniel G. Smith; Frank Wyrowski; Andreas Erdmann, Editor(s)

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