13 March 2018 Advanced defect classification by smart sampling, based on sub-wavelength anisotropic scatterometry
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Abstract
We report on advanced defect classification using TNO’s RapidNano particle scanner. RapidNano was originally designed for defect detection on blank substrates. In detection-mode, the RapidNano signal from nine azimuth angles is added for sensitivity. In review-mode signals from individual angles are analyzed to derive additional defect properties. We define the Fourier coefficient parameter space that is useful to study the statistical variation in defect types on a sample. By selecting defects from each defect type for further review by SEM, information on all defects can be obtained efficiently.
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Peter van der Walle, Peter van der Walle, Esther Kramer, Esther Kramer, Rob Ebeling, Rob Ebeling, Helma Spruit, Helma Spruit, Paul Alkemade, Paul Alkemade, Silvania Pereira, Silvania Pereira, Jacques van der Donck, Jacques van der Donck, Diederik Maas, Diederik Maas, } "Advanced defect classification by smart sampling, based on sub-wavelength anisotropic scatterometry", Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105852D (13 March 2018); doi: 10.1117/12.2297188; https://doi.org/10.1117/12.2297188
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