Recently, a new technique called Fourier normalization has enabled the parametric fitting of optical images with multiple or even a continuum of scattered spatial frequencies. Integral to the performance of this methodology is the characterization of the high magnification imaging microscope used in these experiments. Scatterfield microscopy techniques yield the necessary angular resolution required for determining the effects of the illumination and collection paths upon the electric field within the microscope. A multi-step characterization methodology is presented with experimental examples using a microscope operating at λ = 450 nm. A prior scatterfield characterization technique for specular reflectors is reviewed and shown to be a special case of the newer generalized approach. Possible implications of this methodology for improved critical dimension measurements are assessed.
There has been much recent work in developing advanced optical metrology applications that use imaging optics for
optical critical dimension (OCD) measurements, defect detection, and for potential use with in-die metrology
applications. We have previously reported quantitative measurements for sub-50 nm CD dense arrays which scatter only the 0th-order specular diffraction component using angle-resolved scatterfield microscopy. Through angle-resolved and focus-resolved imaging, we now measure OCD targets with three-dimensional scattered fields that contain multiple Fourier frequencies. Experimental sensitivity to nanometer scale linewidth changes is presented, supported by simulation studies. A new, more advanced approach to tool normalization is coupled with rigorous electromagnetic simulations and library based regression fitting that potentially enables OCD measurements with sub-nanometer uncertainties for targets that scatter multiple Fourier frequencies.
KEYWORDS: 3D metrology, Optical metrology, Polarization, Light scattering, Statistical analysis, 3D image processing, Data acquisition, Metrology, Atomic force microscopy, Silicon
There has been much recent work in developing advanced optical metrology applications that use imaging optics for
critical dimension measurements, defect detection and for potential use with in-die metrology. Sensitivity to nanometer
scale changes has been observed when measuring critical dimensions of sub-wavelength features or when imaging
defects below 20 nm using angle-resolved and focus-resolved optical data. However, these methods inherently involve
complex imaging optics and analysis of complicated three-dimensional electromagnetic fields. This paper will develop a
new approach to enable the rigorous analysis of three-dimensional through-focus optical images. We use rigorous
electromagnetic simulation tools and statistical methods to evaluate sensitivities and uncertainties in the measurement of
three dimensional layouts encountered in critical dimension, contour metrology and defect inspection.
KEYWORDS: Atomic force microscopy, Metrology, Statistical analysis, Data modeling, Reflectivity, Optical testing, Model-based design, 3D modeling, Oxides, Scanning electron microscopy
We present a method to combine measurements from different techniques that reduces uncertainties and can improve
measurement throughput. The approach directly integrates the measurement analysis of multiple techniques that can
include different configurations or platforms. This approach has immediate application 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 with fundamental limitations resulting from the parametric correlations. A
strategy to decouple parametric correlation and reduce measurement uncertainties is described. We develop the rigorous
underlying Bayesian statistical model and apply this methodology to OCD metrology. We then introduce an approach to
damp the regression process to achieve more stable and rapid regression fitting. These methods that use a priori
information are shown to reduce measurement uncertainty and improve throughput while also providing an improved
foundation for comprehensive reference metrology.
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.
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