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.
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 (Presented at SPIE Advanced Lithography: March 01, 2018; Published: 13 March 2018); https://doi.org/10.1117/12.2303487.
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