Context-Based Virtual Metrology

Authored by: Peter Ebersbach, Adam M. Urbanowicz, Dmitriy Likhachev, Carsten Hartig, Michael Shifrin | SPIE 2018, February 1, 2018

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.
Keywords: hybrid metrology, optical modeling, virtual metrology, machine learning, process context, process
commonality, process control