20 April 2011 Nested uncertainties and hybrid metrology to improve measurement accuracy
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Abstract
In this paper we present a method to combine measurement techniques that reduce uncertainties and improve measurement throughput. The approach has immediate utility when performing model-based optical critical dimension (OCD) measurements. When modeling optical measurements, a library of curves is assembled through the simulation of a multi-dimensional parameter space. Parametric correlation and measurement noise lead to measurement uncertainty in the fitting process resulting in fundamental limitations due to parametric correlations. We provide a strategy to decouple parametric correlation and reduce measurement uncertainties. We also develop the rigorous underlying Bayesian statistical model to apply this methodology to OCD metrology. These statistical methods use a priori information rigorously to reduce measurement uncertainty, improve throughput and develop an improved foundation for comprehensive reference metrology.
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R. M. Silver, N. F. Zhang, B. M. Barnes, H. Zhou, J. Qin, R. Dixson, "Nested uncertainties and hybrid metrology to improve measurement accuracy", Proc. SPIE 7971, Metrology, Inspection, and Process Control for Microlithography XXV, 797116 (20 April 2011); doi: 10.1117/12.882411; https://doi.org/10.1117/12.882411
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