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Nested Uncertainties and Hybrid Metrology to Improve Measurement Accuracy



Richard M. Silver, Nien F. Zhang, Bryan M. Barnes, Hui Zhou, Jing Qin, Ronald G. Dixson


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 Title
Metrology Inspection and Process Control
Conference Dates
February 27-March 3, 2011
Conference Location
San Jose, CA


uncertainties, multi-dimensional parameter space, Hybrid metrology, Bayesian statistical mode, optics


Silver, R. , Zhang, N. , Barnes, B. , Zhou, H. , Qin, J. and Dixson, R. (2011), Nested Uncertainties and Hybrid Metrology to Improve Measurement Accuracy, Metrology Inspection and Process Control, San Jose, CA (Accessed June 18, 2024)


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Created April 18, 2011, Updated February 19, 2017