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
, Zhang, N.
, Barnes, B.
, Zhou, H.
, Qin, J.
and Dixson, R.
Nested Uncertainties and Hybrid Metrology to Improve Measurement Accuracy, Metrology Inspection and Process Control, San Jose, CA
(Accessed February 20, 2024)