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Automatic parameter optimization of the local model fitting method for single-shot surface profiling.

Syogo Mori1, Masashi Sugiyama, Hidemitsu Ogawa

  • 1Tokyo Institute of Technology, 2-12-1 O-okayama, Tokyo, 152-8552, Japan. mori.s.ag@m.titech.ac.jp

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Summary
This summary is machine-generated.

This study introduces an automated method for local model fitting (LMF) surface profiling. It removes the need for manual tuning by automatically selecting optimal local regions for accurate 3D surface measurements.

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Area of Science:

  • Metrology
  • Optical Engineering
  • Surface Science

Background:

  • Local Model Fitting (LMF) is a single-shot surface profiling technique.
  • LMF relies on the assumption of local surface flatness within an interference image.
  • Accurate measurements depend on appropriate selection of the local area size and shape.

Purpose of the Study:

  • To develop an automated method for determining the optimal local region shape and size for LMF.
  • To eliminate the need for prior surface knowledge or manual parameter tuning in LMF.
  • To enhance the robustness and applicability of single-shot surface profiling.

Main Methods:

  • A novel algorithm is proposed to automatically identify and define local regions within a single interference image.
  • The method analyzes pixel information to determine optimal parameters for LMF.
  • Experimental validation is performed to assess the effectiveness of the automated approach.

Main Results:

  • The proposed method successfully automates the determination of local region parameters for LMF.
  • Experimental results demonstrate the effectiveness of the automated LMF approach.
  • Accurate surface profiling is achieved without manual intervention or prior surface data.

Conclusions:

  • Automated local region determination significantly improves the usability of LMF.
  • The developed method offers a robust solution for single-shot surface profiling.
  • This advancement facilitates more efficient and accurate 3D surface measurements.