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12 November 2015 Optimizing hybrid metrology: rigorous implementation of Bayesian and combined regression
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Hybrid metrology, e.g., the combination of several measurement techniques to determine critical dimensions, is an increasingly important approach to meet the needs of the semiconductor industry. A proper use of hybrid metrology may yield not only more reliable estimates for the quantitative characterization of three-dimensional (3-D) structures but also a more realistic estimation of the corresponding uncertainties. Recent developments at the National Institute of Standards and Technology feature the combination of optical critical dimension measurements and scanning electron microscope results. The hybrid methodology offers the potential to make measurements of essential 3-D attributes that may not be feasible otherwise. 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.
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," Journal of Micro/Nanolithography, MEMS, and MOEMS 14(4), 044001 (12 November 2015).
Published: 12 November 2015

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