KEYWORDS: Semiconducting wafers, Etching, Education and training, Chemical mechanical planarization, Metrology, Machine learning, Data modeling, Process control, Time metrology, Visibility
Optical Critical Dimension (OCD) spectroscopy is a reliable, non-destructive, and high-throughput measurement technique for metrology and process control that is widely used in semiconductor fabrication facilities (fabs). Wafers are sampled sparsely in-line, and measured at about 10-20 predetermined locations, to extract geometrical parameters of interest. Traditionally, these parameters were deduced by solving Maxwell’s equations for the specific film stack geometry. Recently advanced machine learning (ML) models, or combinations of ML and geometric models, has become increasingly attractive due to the several advantages of this approach. Advanced node processes can benefit from more extensive data sampling, but this conflicts with measurement cycle time goals and overall metrology tool costs, which cause fabs to use sparse sampling schemes. In this paper, we introduce a novel methodology that allows wafers to be sampled sparsely but provides the parameters of interest as if they were densely measured. We show how such a methodology allows us to increase data output with no impact on overall measurement time, while maintaining high accuracy and robustness. Such a capability has potentially far-reaching implications for improved process control and faster yield learning in semiconductor process development.
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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