Advanced machine learning eco-system to address HVM optical metrology requirements

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

doi:10.1117/12.2552058

20 March 2020 Advanced machine learning eco-system to address HVM optical metrology requirements

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

Author Affiliations +

Proceedings Volume 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV; 113251H (2020) https://doi.org/10.1117/12.2552058
Event: SPIE Advanced Lithography, 2020, San Jose, California, United States

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.

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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, and Igor Turovets "Advanced machine learning eco-system to address HVM optical metrology requirements", Proc. SPIE 11325, Metrology, Inspection, and Process Control for Microlithography XXXIV, 113251H (20 March 2020); https://doi.org/10.1117/12.2552058

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