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

Peter Van Der Walle; Esther Kramer; Rob Ebeling; Helma Spruit; Paul Alkemade; Silvania Pereira; Jacques Van Der Donck; Diederik Maas

doi:10.1117/12.2297188

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

Peter Van Der Walle, Esther Kramer, Rob Ebeling, Helma Spruit, Paul Alkemade, Silvania Pereira, Jacques Van Der Donck, Diederik Maas

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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.

Original language	English
Title of host publication	Metrology, Inspection, and Process Control for Microlithography XXXII
Editors	Vladimir A. Ukraintsev, Ofer Adan
Publisher	SPIE
Volume	10585
ISBN (Electronic)	9781510616622
DOIs	https://doi.org/10.1117/12.2297188
Publication status	Published - 2018
Event	Metrology, Inspection, and Process Control for Microlithography XXXII 2018 - San Jose, United States Duration: 26 Feb 2018 → 1 Mar 2018

Conference

Conference	Metrology, Inspection, and Process Control for Microlithography XXXII 2018
Country/Territory	United States
City	San Jose
Period	26/02/18 → 1/03/18

Keywords

ADC
advanced defect classification
dark field microscopy
defect detection
defect review
latex sphere equivalent
Particle contamination
redetection
scatterometry
SEM
semiconductor

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10.1117/12.2297188

Cite this

Van Der Walle, P., Kramer, E., Ebeling, R., Spruit, H., Alkemade, P., Pereira, S., Van Der Donck, J., & Maas, D. (2018). Advanced defect classification by smart sampling, based on sub-wavelength anisotropic scatterometry. In V. A. Ukraintsev, & O. Adan (Eds.), Metrology, Inspection, and Process Control for Microlithography XXXII (Vol. 10585). Article 105852D SPIE. https://doi.org/10.1117/12.2297188

@inproceedings{983ac99a92ea43cda338a6cf01b2114d,

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

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.",

keywords = "ADC, advanced defect classification, dark field microscopy, defect detection, defect review, latex sphere equivalent, Particle contamination, redetection, scatterometry, SEM, semiconductor",

author = "{Van Der Walle}, Peter and Esther Kramer and Rob Ebeling and Helma Spruit and Paul Alkemade and Silvania Pereira and {Van Der Donck}, Jacques and Diederik Maas",

year = "2018",

doi = "10.1117/12.2297188",

language = "English",

volume = "10585",

editor = "Ukraintsev, {Vladimir A.} and Adan, {Ofer }",

booktitle = "Metrology, Inspection, and Process Control for Microlithography XXXII",

publisher = "SPIE",

address = "United States",

note = "Metrology, Inspection, and Process Control for Microlithography XXXII 2018 ; Conference date: 26-02-2018 Through 01-03-2018",

}

Van Der Walle, P, Kramer, E, Ebeling, R, Spruit, H, Alkemade, P, Pereira, S, Van Der Donck, J & Maas, D 2018, Advanced defect classification by smart sampling, based on sub-wavelength anisotropic scatterometry. in VA Ukraintsev & O Adan (eds), Metrology, Inspection, and Process Control for Microlithography XXXII. vol. 10585, 105852D, SPIE, Metrology, Inspection, and Process Control for Microlithography XXXII 2018, San Jose, United States, 26/02/18. https://doi.org/10.1117/12.2297188

Advanced defect classification by smart sampling, based on sub-wavelength anisotropic scatterometry. / Van Der Walle, Peter; Kramer, Esther; Ebeling, Rob et al.
Metrology, Inspection, and Process Control for Microlithography XXXII. ed. / Vladimir A. Ukraintsev; Ofer Adan. Vol. 10585 SPIE, 2018. 105852D.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Advanced defect classification by smart sampling, based on sub-wavelength anisotropic scatterometry

AU - Van Der Walle, Peter

AU - Kramer, Esther

AU - Ebeling, Rob

AU - Spruit, Helma

AU - Alkemade, Paul

AU - Pereira, Silvania

AU - Van Der Donck, Jacques

AU - Maas, Diederik

PY - 2018

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N2 - 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.

AB - 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.

KW - ADC

KW - advanced defect classification

KW - dark field microscopy

KW - defect detection

KW - defect review

KW - latex sphere equivalent

KW - Particle contamination

KW - redetection

KW - scatterometry

KW - SEM

KW - semiconductor

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DO - 10.1117/12.2297188

M3 - Conference contribution

AN - SCOPUS:85046418257

VL - 10585

BT - Metrology, Inspection, and Process Control for Microlithography XXXII

A2 - Ukraintsev, Vladimir A.

A2 - Adan, Ofer

PB - SPIE

T2 - Metrology, Inspection, and Process Control for Microlithography XXXII 2018

Y2 - 26 February 2018 through 1 March 2018

ER -

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

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