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

conference paper
2018
Walle, P. van der

Kramer, E.

Ebeling, R.P.

Spruit, W.E.T.

Alkemade, P.

Pereira, S.

Donck, J.C.J. van der

Maas, D.J.
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.
Topics
ADCAdvanced defect classificationDark field microscopyDefect detectionDefect reviewLatex sphere equivalentParticle contaminationRedetectionScatterometrySEMSemiconductorNanotechnology
TNO Identifier
788788
Repository link
https://resolver.tno.nl/uuid:dd0458f2-c461-4f88-8321-3c7bd6c86ec2
ISSN
0277786X
ISBN
9781510616622
Publisher
SPIE
Article nr.
105852D
Source title
Metrology, Inspection, and Process Control for Microlithography XXXII 2018, 26 February - 1 March 2018, San Jose, CA, USA
Editor(s)
Ukraintsev, V.A.
Adan, O.
Collation
7 p.
Files
To receive the publication files, please send an e-mail request to TNO Repository.