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Proceedings Paper

Deep learning nanometrology of line edge roughness
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Paper Abstract

Deep Learning (DL) techniques based on Denoising Convolutional Neural Networks (DeCNN) are applied in the denoising of SEM images of line patterns to contribute to noise-reduced (unbiased) LER nanometrology. The models of DeCNN are trained in a sufficiently large set of synthesized SEM images with controlled Gaussian and Poisson noise level. Due to the image-based nature of the DL approach, it can be combined sequentially with the state of the art PSD-based method especially for highly noisy images where the use of the PSD-based method alone fails. The results for test synthesized images show the high predicting capability of the DL assisted method for the commonly used LER parameters and functions (Rms, ξ, α, PSD) of the true (zero-noise) values revealing its potential for future use toward an unbiased LER metrology.

Paper Details

Date Published: 26 March 2019
PDF: 11 pages
Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 1095920 (26 March 2019); doi: 10.1117/12.2520941
Show Author Affiliations
Eva Giannatou, Nanometrisis p.c. (Greece)
Institute for Langauge and Speech Processing (Greece)
Vassilios Constantoudis, Institute of Nanoscience and Nanotechnology (Greece)
Nanometrisis p.c. (Greece)
Institute of Nanoscience and Nanotechnology, NCSR Demokritos (Greece)
George Papavieros, Nanometrisis p.c. (Greece)
Institute of Nanoscience and Nanotechnology (Greece)
Univ. of Thessaloniki (Greece)
Harria Papagrorgiou, Institute for Language and Speech Processing (Greece)
Gian Francesco Lorusso, IMEC (Belgium)
Vito Rutigliani, IMEC (Belgium)
Frieda van Roey, IMEC (Belgium)
Evangelos Gogolides, Nanometrisis p.c. (Greece)
Institute of Nanoscience and Nanotechnology (Greece)


Published in SPIE Proceedings Vol. 10959:
Metrology, Inspection, and Process Control for Microlithography XXXIII
Vladimir A. Ukraintsev; Ofer Adan, Editor(s)

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