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Understanding overlay signatures using machine learning on non-lithography context information
Author(s): Marshall Overcast; Corey Mellegaard; David Daniel; Boris Habets; Georg Erley; Steffen Guhlemann; Xaver Thrun; Stefan Buhl; Steven Tottewitz
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Paper 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.

Paper Details

Date Published: 13 March 2018
PDF: 15 pages
Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105851U (13 March 2018); doi: 10.1117/12.2303487
Show Author Affiliations
Marshall Overcast, IM Flash Technologies, LLC (United States)
Corey Mellegaard, IM Flash Technologies, LLC (United States)
David Daniel, IM Flash Technologies, LLC (United States)
Boris Habets, Qoniac GmbH (Germany)
Georg Erley, Qoniac GmbH (Germany)
Steffen Guhlemann, Qoniac GmbH (Germany)
Xaver Thrun, Qoniac GmbH (Germany)
Stefan Buhl, Qoniac GmbH (Germany)
Steven Tottewitz, Qoniac GmbH (Germany)


Published in SPIE Proceedings Vol. 10585:
Metrology, Inspection, and Process Control for Microlithography XXXII
Vladimir A. Ukraintsev, Editor(s)

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