Context-based virtual metrology

Peter Ebersbach; Adam M. Urbanowicz; Dmitriy Likhachev; Carsten Hartig; Michael Shifrin

doi:10.1117/12.2302498

22 March 2018 Context-based virtual metrology

Peter Ebersbach, Adam M. Urbanowicz, Dmitriy Likhachev, Carsten Hartig, Michael Shifrin

Proceedings Volume 10585, Metrology, Inspection, and Process Control for Microlithography XXXII; 1058514 (2018) https://doi.org/10.1117/12.2302498
Event: SPIE Advanced Lithography, 2018, San Jose, California, United States

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

Citation Download Citation

Peter Ebersbach, Adam M. Urbanowicz, Dmitriy Likhachev, Carsten Hartig, and Michael Shifrin "Context-based virtual metrology", Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 1058514 (22 March 2018); https://doi.org/10.1117/12.2302498

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