Privacy preserving amalgamated machine learning for process control

Wilfried Verachtert; Thomas J. Ashby; Imen Chakroun; Roel Wuyts; Sayantan Das; Sandip Halder; Philippe Leray

doi:10.1117/12.2584893

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22 February 2021 Privacy preserving amalgamated machine learning for process control

Wilfried Verachtert, Thomas J. Ashby, Imen Chakroun, Roel Wuyts, Sayantan Das, Sandip Halder, Philippe Leray

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Proceedings Volume 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV; 116111F (2021) https://doi.org/10.1117/12.2584893
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

Further application of machine learning is important for the future development of semiconductor fabrication. Machine learning relies on access to large, detailed datasets. When different parts of the data are owned by different companies who do not wish to pool their data due to commercial sensitivity concerns, the benefits of machine learning can be limited resulting in reduced manufacturing performance. Imec has developed Privacy-preserving Amalgamated Machine Learning (PAML) to overcome this problem and achieve predictive performance close to models built on pooled data, without compromising sensitive raw data. In this paper we give a concrete example based on an in-house overlay metrology dataset where we apply a PAML enhanced version of a tree regression model, and quantify the performance benefit compared to separate models that don’t have access to all of the data.

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Wilfried Verachtert, Thomas J. Ashby, Imen Chakroun, Roel Wuyts, Sayantan Das, Sandip Halder, and Philippe Leray "Privacy preserving amalgamated machine learning for process control", Proc. SPIE 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 116111F (22 February 2021); https://doi.org/10.1117/12.2584893

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