Deep learning nanometrology of line edge roughness

Eva Giannatou; Vassilios Constantoudis; George Papavieros; Harria Papagrorgiou; Gian Francesco Lorusso; Vito Rutigliani; Frieda van Roey; Evangelos Gogolides

doi:10.1117/12.2520941

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26 March 2019 Deep learning nanometrology of line edge roughness

Eva Giannatou, Vassilios Constantoudis, George Papavieros, Harria Papagrorgiou, Gian Francesco Lorusso, Vito Rutigliani, Frieda van Roey, Evangelos Gogolides

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Proceedings Volume 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII; 1095920 (2019) https://doi.org/10.1117/12.2520941
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

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.

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Eva Giannatou, Vassilios Constantoudis, George Papavieros, Harria Papagrorgiou, Gian Francesco Lorusso, Vito Rutigliani, Frieda van Roey, and Evangelos Gogolides "Deep learning nanometrology of line edge roughness", Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 1095920 (26 March 2019); https://doi.org/10.1117/12.2520941

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