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Optimizing Hybrid Metrology: Rigorous Implementation of Bayesian and Combined Regression.



Mark Alexander Henn, Richard M. Silver, John S. Villarrubia, Nien F. Zhang, Hui Zhou, Bryan M. Barnes, Andras Vladar, Bin Ming


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 2 function that is minimized. Both hypothetical examples and measurement data are used to illustrate solutions to these challenges.
Journal of Micro/Nanolithography, MEMS, and MOEMS


hybrid metrology, electromagnetic simulation, sensitivity and uncertainty evaluation, Bayesian data analysis.


, M. , Silver, R. , Villarrubia, J. , Zhang, N. , Zhou, H. , Barnes, B. , Vladar, A. and Ming, B. (2015), Optimizing Hybrid Metrology: Rigorous Implementation of Bayesian and Combined Regression., Journal of Micro/Nanolithography, MEMS, and MOEMS, [online], (Accessed July 17, 2024)


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Created November 12, 2015, Updated November 10, 2018