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Publication Citation: Nested Uncertainties and Hybrid Metrology to Improve Measurement Accuracy

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Author(s): Richard M. Silver; Nien F. Zhang; Bryan M. Barnes; Hui Zhou; Jing Qin; Ronald G. Dixson;
Title: Nested Uncertainties and Hybrid Metrology to Improve Measurement Accuracy
Published: April 18, 2011
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 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
Proceedings: Metrology Inspection and Process Control
Volume: 7971
Pages: pp. 797116-1 - 797116-11
Location: San Jose, CA
Dates: February 27-March 3, 2011
Keywords: uncertainties, multi-dimensional parameter space, Hybrid metrology, Bayesian statistical mode, optics
Research Areas: Optical Physics, Theoretical Computation and Modeling