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Application of principal component analysis to EUV multilayer defect printing

 
: Xu, D.; Evanschitzky, P.; Erdmann, A.

:

Smith, D.G. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Optical Systems Design 2015. Computational Optics : 7-9 September 2015, Jena, Germany
Bellingham, WA: SPIE, 2015 (Proceedings of SPIE 9630)
ISBN: 978-1-62841-819-4
Paper 96300Y, 11 pp.
Conference "Optical Systems Design - Computational Optics" <2015, Jena>
English
Conference Paper
Fraunhofer IISB ()

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.

: http://publica.fraunhofer.de/documents/N-382392.html