Optimizing hybrid metrology: rigorous implementation of Bayesian and combined regression

Mark-Alexander Henn; Richard M. Silver; John S. Villarrubia; Nien Fan Zhang; Hui Zhou; Bryan M. Barnes; Bin Ming; András E. Vladár

doi:10.1117/12.2175653

19 March 2015 Optimizing hybrid metrology: rigorous implementation of Bayesian and combined regression

Mark-Alexander Henn, Richard M. Silver, John S. Villarrubia, Nien Fan Zhang, Hui Zhou, Bryan M. Barnes, Bin Ming, András E. Vladár

Author Affiliations +

Proceedings Volume 9424, Metrology, Inspection, and Process Control for Microlithography XXIX; 94241J (2015) https://doi.org/10.1117/12.2175653
Event: SPIE Advanced Lithography, 2015, San Jose, California, United States

Abstract

Hybrid metrology, e.g. the combination of several measurement techniques to determine critical dimensions, is an important approach to meet the needs of semiconductor industry. A proper use of hybrid metrology may not only yield more reliable estimates for the quantitative characterization of 3-D structures but also a more realistic estimation of the corresponding uncertainties. Recent developments at the National Institute of Standards and Technology (NIST) feature the combination of optical critical dimension (OCD) measurements and scanning electron microscope (SEM) results. The hybrid methodology offers the potential to make measurements of essential 3-D attributes that may not be otherwise feasible. However, combining techniques gives rise to essential challenges in error analysis and comparing results from different instrument models, especially the effect of systematic and highly correlated errors in the measurement on the ¬χ² function that is minimized. Both hypothetical examples and measurement data are used to illustrate solutions to these challenges.

Citation Download Citation

Mark-Alexander Henn, Richard M. Silver, John S. Villarrubia, Nien Fan Zhang, Hui Zhou, Bryan M. Barnes, Bin Ming, and András E. Vladár "Optimizing hybrid metrology: rigorous implementation of Bayesian and combined regression", Proc. SPIE 9424, Metrology, Inspection, and Process Control for Microlithography XXIX, 94241J (19 March 2015); https://doi.org/10.1117/12.2175653

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