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Neural Network Approach for Modelling and Compensation of Local Surface-Tilting-Dependent Topography Measurement Errors in Coherence Scanning Interferometry

Metrology, ISSN: 2673-8244, Vol: 4, Issue: 3, Page: 446-456
2024
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    Citations
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    Usage
  • 1
    Captures
  • 2
    Mentions
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    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Captures
    1
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • 1

Most Recent News

Researcher from Physikalisch-Technische Bundesanstalt Publishes Findings in Metrology (Neural Network Approach for Modelling and Compensation of Local Surface-Tilting-Dependent Topography Measurement Errors in Coherence Scanning Interferometry)

2024 SEP 19 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Science Daily -- Investigators publish new report on metrology. According to news

Article Description

The topography measurement accuracy of coherence scanning interferometry (CSI) suffers from the local characteristic of micro-structured surfaces, such as local surface slopes. A cylindrical reference artefact made of single-mode fiber with high roundness and low roughness has been proposed in this manuscript to traceably investigate the surface tilting induced measurement deviations using coherence scanning interferometry with high NA objectives. A feed-forward neural network (FF-NN) is designed and trained to model and thereafter compensate the systematic measurement deviations due to local surface tilting. Experimental results have verified that the FF-NN approach can well enhance the accuracy of the CSI for radius measurement of cylindrical samples up to 0.3%. Further development of the FF-NN for modelling of the measurement errors in CSI due to the optical properties of surfaces including areal roughness is outlined.

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