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Proceedings Paper

Advanced machine learning eco-system to address HVM optical metrology requirements
Author(s): Padraig Timoney; Roma Luthra; Alex Elia; Haibo Liu; Paul Isbester; Avi Levy; Michael Shifrin; Barak Bringoltz; Eylon Rabinovich; Ariel Broitman; Eitan Rothstein; Ran Yacoby; Ilya Rubinovich; YongHa Kim; Ofer Shlagman; Barak Ben-Nahum; Marina Zolkin; Igor Turovets
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Paper Abstract

Machine learning (ML) techniques have been successfully deployed to resolve optical metrology challenges in semiconductor industry during recent years. With more advanced computing technology and algorithms, the ML system can be improved further to address High Volume Manufacturing (HVM) requirements. In this work, an advanced ML eco-system was implemented based on big data architecture to generate fast and user-friendly ML predictive models for metrology purposes. Application work and results completed by using this ML eco-system have revealed its capability to quickly refine solutions to predict both external reference data and to improve the throughput of conventional Optical Critical Dimension (OCD) metrology. The time-to-solution has been significantly improved and human operational time has also been greatly reduced. Results were shown for both front end and back end of line measurement applications, demonstrating good correlations and small errors in comparison with either external reference or conventional OCD results. The incremental retraining from this ML eco-system improved the correlation to external references, and multiple retrained models were analyzed to understand retraining effects and corresponding requirements. Quality Metric (QM) was also shown to have relevance in monitoring recipe performance. It has successfully demonstrated that with this advanced ML eco-system, streamlined ML models can be readily updated for high sensitivity and process development applications in HVM scenarios.

Paper Details

Date Published: 20 March 2020
PDF: 10 pages
Proc. SPIE 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV, 113251H (20 March 2020); doi: 10.1117/12.2552058
Show Author Affiliations
Padraig Timoney, GLOBALFOUNDRIES Inc. (United States)
Roma Luthra, GLOBALFOUNDRIES Inc. (United States)
Alex Elia, GLOBALFOUNDRIES Inc. (United States)
Haibo Liu, Nova Measuring Instruments Inc. (United States)
Paul Isbester, Nova Measuring Instruments Inc. (United States)
Avi Levy, Nova Measuring Instruments Inc. (United States)
Michael Shifrin, Nova Measuring Instruments Ltd. (Israel)
Barak Bringoltz, Nova Measuring Instruments Ltd. (Israel)
Eylon Rabinovich, Nova Measuring Instruments Ltd. (Israel)
Ariel Broitman, Nova Measuring Instruments Ltd. (Israel)
Eitan Rothstein, Nova Measuring Instruments Ltd. (Israel)
Ran Yacoby, Nova Measuring Instruments Ltd. (Israel)
Ilya Rubinovich, Nova Measuring Instruments Ltd. (Israel)
YongHa Kim, Nova Measuring Instruments Ltd. (Israel)
Ofer Shlagman, Nova Measuring Instruments Ltd. (Israel)
Barak Ben-Nahum, Nova Measuring Instruments Ltd. (Israel)
Marina Zolkin, Nova Measuring Instruments Ltd. (Israel)
Igor Turovets, Nova Measuring Instruments Ltd. (Israel)

Published in SPIE Proceedings Vol. 11325:
Metrology, Inspection, and Process Control for Microlithography XXXIV
Ofer Adan; John C. Robinson, Editor(s)

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