Understanding overlay signatures using machine learning on non-lithography context information

Marshall Overcast; Corey Mellegaard; David Daniel; Boris Habets; Georg Erley; Steffen Guhlemann; Xaver Thrun; Stefan Buhl; Steven Tottewitz

doi:10.1117/12.2303487

13 March 2018 Understanding overlay signatures using machine learning on non-lithography context information

Marshall Overcast, Corey Mellegaard, David Daniel, Boris Habets, Georg Erley, Steffen Guhlemann, Xaver Thrun, Stefan Buhl, Steven Tottewitz

Author Affiliations +

Proceedings Volume 10585, Metrology, Inspection, and Process Control for Microlithography XXXII; 105851U (2018) https://doi.org/10.1117/12.2303487
Event: SPIE Advanced Lithography, 2018, San Jose, California, United States

Abstract

Overlay errors between two layers can be caused by non-lithography processes. While these errors can be compensated by the run-to-run system, such process and tool signatures are not always stable. In order to monitor the impact of non-lithography context on overlay at regular intervals, a systematic approach is needed. Using various machine learning techniques, significant context parameters that relate to deviating overlay signatures are automatically identified. Once the most influential context parameters are found, a run-to-run simulation is performed to see how much improvement can be obtained. The resulting analysis shows good potential for reducing the influence of hidden context parameters on overlay performance. Non-lithographic contexts are significant contributors, and their automatic detection and classification will enable the overlay roadmap, given the corresponding control capabilities.

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Citation Download Citation

Marshall Overcast, Corey Mellegaard, David Daniel, Boris Habets, Georg Erley, Steffen Guhlemann, Xaver Thrun, Stefan Buhl, and Steven Tottewitz "Understanding overlay signatures using machine learning on non-lithography context information", Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105851U (13 March 2018); https://doi.org/10.1117/12.2303487

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