Advanced defect classification by smart sampling, based on sub-wavelength anisotropic scatterometry

Conference paper (2018)

Authors

P.F.A. Alkemade QN/Kavli Nanolab Delft - AS
S.F. Pereira ImPhys/Optics - AS
Research Group
QN/Kavli Nanolab Delft (AS) (TU Delft)
SEM ADC Semiconductor Scatterometry Dark field microscopy Defect detection Defect review Advanced defect classification Latex sphere equivalent Particle contamination Redetection
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Applied Sciences

Department

QN/Quantum Nanoscience

Research Group

QN/Kavli Nanolab Delft

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.

Files

105852D.pdf
(.pdf | 0.602 Mb)