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Proceedings Paper

Image-based pupil plane characterization via principal component analysis for EUVL tools
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Paper Abstract

We present an approach to image-based pupil plane amplitude and phase characterization using models built with principal component analysis (PCA). PCA is a statistical technique to identify the directions of highest variation (principal components) in a high-dimensional dataset. A polynomial model is constructed between the principal components of through-focus intensity for the chosen binary mask targets and pupil amplitude or phase variation. This method separates model building and pupil characterization into two distinct steps, thus enabling rapid pupil characterization following data collection. The pupil plane variation of a zone-plate lens from the Semiconductor High-NA Actinic Reticle Review Project (SHARP) at Lawrence Berkeley National Laboratory will be examined using this method. Results will be compared to pupil plane characterization using a previously proposed methodology where inverse solutions are obtained through an iterative process involving least-squares regression.

Paper Details

Date Published: 18 March 2016
PDF: 11 pages
Proc. SPIE 9776, Extreme Ultraviolet (EUV) Lithography VII, 977618 (18 March 2016); doi: 10.1117/12.2219745
Show Author Affiliations
Zac Levinson, Rochester Institute of Technology (United States)
Andrew Burbine, Rochester Institute of Technology (United States)
Erik Verduijn, GLOBALFOUNDRIES Inc. (United States)
Obert Wood, GLOBALFOUNDRIES Inc. (United States)
Pawitter Mangat, GLOBALFOUNDRIES Inc. (United States)
Kenneth A. Goldberg, Lawrence Berkeley National Lab. (United States)
Markus P. Benk, Lawrence Berkeley National Lab. (United States)
Antoine Wojdyla, Lawrence Berkeley National Lab. (United States)
Bruce W. Smith, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 9776:
Extreme Ultraviolet (EUV) Lithography VII
Eric M. Panning, Editor(s)

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