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20 April 2011 Nested uncertainties and hybrid metrology to improve measurement accuracy
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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.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
R. M. Silver, N. F. Zhang, B. M. Barnes, H. Zhou, J. Qin, and 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);

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