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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 25 — Sep. 1, 2012
  • pp: 6196–6206

Improving optical measurement uncertainty with combined multitool metrology using a Bayesian approach

Nien Fan Zhang, Richard M. Silver, Hui Zhou, and Bryan M. Barnes  »View Author Affiliations

Applied Optics, Vol. 51, Issue 25, pp. 6196-6206 (2012)

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Recently, there has been significant research investigating new optical technologies for dimensional metrology of features 22 nm in critical dimension and smaller. When modeling optical measurements, a library of curves is assembled through the simulation of a multidimensional parameter space. A nonlinear regression routine described in this paper is then used to identify an optimum set of parameters that yields the closest experiment-to-theory agreement. However, parametric correlation, measurement noise, and model inaccuracy all lead to measurement uncertainty in the fitting process for optical critical dimension measurements. To improve the optical measurements, other techniques such as atomic force microscopy and scanning electronic microscopy can also be used to provide supplemental a priori information. In this paper, a Bayesian statistical approach is proposed to allow the combination of different measurement techniques that are based on different physical measurements. The effect of this hybrid metrology approach will be shown to reduce the uncertainties of the parameter estimators.

OCIS Codes
(120.3940) Instrumentation, measurement, and metrology : Metrology
(180.5810) Microscopy : Scanning microscopy
(050.1755) Diffraction and gratings : Computational electromagnetic methods
(050.6624) Diffraction and gratings : Subwavelength structures

ToC Category:
Instrumentation, Measurement, and Metrology

Original Manuscript: January 11, 2012
Revised Manuscript: June 14, 2012
Manuscript Accepted: July 20, 2012
Published: August 30, 2012

Nien Fan Zhang, Richard M. Silver, Hui Zhou, and Bryan M. Barnes, "Improving optical measurement uncertainty with combined multitool metrology using a Bayesian approach," Appl. Opt. 51, 6196-6206 (2012)

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