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

Zac Levinson; Andrew Burbine; Erik Verduijn; Obert Wood; Pawitter Mangat; Kenneth A. Goldberg; Markus P. Benk; Antoine Wojdyla; Bruce W. Smith

doi:10.1117/12.2219745

18 March 2016 Image-based pupil plane characterization via principal component analysis for EUVL tools

Zac Levinson, Andrew Burbine, Erik Verduijn, Obert Wood, Pawitter Mangat, Kenneth A. Goldberg, Markus P. Benk, Antoine Wojdyla, Bruce W. Smith

Author Affiliations +

Proceedings Volume 9776, Extreme Ultraviolet (EUV) Lithography VII; 977618 (2016) https://doi.org/10.1117/12.2219745
Event: SPIE Advanced Lithography, 2016, San Jose, California, United States

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.

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Zac Levinson, Andrew Burbine, Erik Verduijn, Obert Wood, Pawitter Mangat, Kenneth A. Goldberg, Markus P. Benk, Antoine Wojdyla, and Bruce W. Smith "Image-based pupil plane characterization via principal component analysis for EUVL tools", Proc. SPIE 9776, Extreme Ultraviolet (EUV) Lithography VII, 977618 (18 March 2016); https://doi.org/10.1117/12.2219745

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