In-line XPS metrology using unsupervised machine learning in high volume manufacturing

Paul K. Isbester; Ganesh Subramanian; Padraig Timoney; Taher Kagalwala; Dmitry Kislitsyn; Heath Pois; Mark Klare; Daniel Kandel; Michal Yachini; Wei Ti Lee; Vanessa Zhang; Mitch Shiver; Saurabh Singh; Parker Lund

doi:10.1117/12.2614116

13 June 2022 In-line XPS metrology using unsupervised machine learning in high volume manufacturing

Paul K. Isbester, Ganesh Subramanian, Padraig Timoney, Taher Kagalwala, Dmitry Kislitsyn, Heath Pois, Mark Klare, Daniel Kandel, Michal Yachini, Wei Ti Lee, Vanessa Zhang, Mitch Shiver, Saurabh Singh, Parker Lund

Author Affiliations +

Proceedings Volume PC12053, Metrology, Inspection, and Process Control XXXVI; PC120530M (2022) https://doi.org/10.1117/12.2614116
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Abstract

To further enhance performance of in-line XPS metrology we will demonstrate the benefit of an unsupervised machine learning approach to increase precision of critical metal gate film thickness measurements and quantification of doping concentration within source-drain junctions. Unsupervised ML efficiently separates process information from inherent noise in the XPS spectra to enable a noise-filtering that improves result precision. The observed precision improvements were utilized to increase wafer through-put by reducing the acquisition time while preserving precision, accuracy, and sensitivity when supporting high volume manufacturing.

Conference Presentation

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Paul K. Isbester, Ganesh Subramanian, Padraig Timoney, Taher Kagalwala, Dmitry Kislitsyn, Heath Pois, Mark Klare, Daniel Kandel, Michal Yachini, Wei Ti Lee, Vanessa Zhang, Mitch Shiver, Saurabh Singh, and Parker Lund "In-line XPS metrology using unsupervised machine learning in high volume manufacturing", Proc. SPIE PC12053, Metrology, Inspection, and Process Control XXXVI, PC120530M (13 June 2022); https://doi.org/10.1117/12.2614116

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