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Advanced Machine Learning Ecosystem to Address HVM Optical Metrology Requirements

February 1, 2020 @ SPIE 2020
Authored by: 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


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 ecosystem 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.

Keywords: Machine learning, Optical Critical Dimension (OCD), big data, High Volume Manufacturing (HVM), incremental retraining, correlation, time to solution, Quality Metric (QM)