22 March 2018 Context-based virtual metrology
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Abstract
Hybrid and data feed forward methodologies are well established for advanced optical process control solutions in highvolume semiconductor manufacturing. Appropriate information from previous measurements, transferred into advanced optical model(s) at following step(s), provides enhanced accuracy and exactness of the measured topographic (thicknesses, critical dimensions, etc.) and material parameters. In some cases, hybrid or feed-forward data are missed or invalid for dies or for a whole wafer. We focus on approaches of virtual metrology to re-create hybrid or feed-forward data inputs in high-volume manufacturing. We discuss missing data inputs reconstruction which is based on various interpolation and extrapolation schemes and uses information about wafer’s process history. Moreover, we demonstrate data reconstruction approach based on machine learning techniques utilizing optical model and measured spectra. And finally, we investigate metrics that allow one to assess error margin of virtual data input.
Conference Presentation
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Peter Ebersbach, Peter Ebersbach, Adam M. Urbanowicz, Adam M. Urbanowicz, Dmitriy Likhachev, Dmitriy Likhachev, Carsten Hartig, Carsten Hartig, Michael Shifrin, Michael Shifrin, "Context-based virtual metrology", Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 1058514 (22 March 2018); doi: 10.1117/12.2302498; https://doi.org/10.1117/12.2302498
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