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 volumeConference contributionScientificpeer-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 languageEnglish
Title of host publicationMetrology, Inspection, and Process Control for Microlithography XXXII
EditorsVladimir A. Ukraintsev, Ofer Adan
PublisherSPIE
Volume10585
ISBN (Electronic)9781510616622
DOIs
Publication statusPublished - 2018
EventMetrology, Inspection, and Process Control for Microlithography XXXII 2018 - San Jose, United States
Duration: 26 Feb 20181 Mar 2018

Conference

ConferenceMetrology, Inspection, and Process Control for Microlithography XXXII 2018
CountryUnited States
CitySan Jose
Period26/02/181/03/18

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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