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Evaluating sharpness functions for automated scanning electron microscopy
M E Rudnaya1, R M M Mattheij, J M L Maubach
1Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands. m.rudnaya@tue.nl
Journal of Microscopy
|November 6, 2010
Summary
Fast autofocus techniques are crucial for automated scanning electron microscopy (SEM). Image derivative and Fourier transform methods outperform other techniques, with parameter tuning significantly improving autofocus quality.
Area of Science:
- Microscopy
- Image Analysis
- Automation Technology
Background:
- Automated scanning electron microscopy (SEM) relies on efficient autofocusing.
- Developing fast and reliable autofocus techniques is essential for high-throughput SEM applications.
- Existing autofocus methods vary in performance across different sample types and image geometries.
Purpose of the Study:
- To evaluate and compare various autofocus techniques for scanning electron microscopy (SEM).
- To identify the most effective autofocus strategies for diverse SEM imaging conditions.
- To investigate the impact of parameter optimization on autofocus performance.
Main Methods:
- Application of multiple autofocus techniques to experimental through-focus series of SEM images.
- Systematic quality evaluation procedure for assessing autofocus performance.
- Comparison of derivative-based, Fourier transform-based, statistical, intensity, and histogram-based methods.
Main Results:
- Autofocus techniques utilizing image derivatives and Fourier transforms demonstrated superior performance.
- Derivative and Fourier transform methods generally outperformed statistical, intensity, and histogram-based approaches.
- Optimization of an additional parameter significantly enhanced the quality of autofocus results.
Conclusions:
- Image derivative and Fourier transform-based autofocus techniques are recommended for automated SEM.
- Parameter tuning is a critical factor for maximizing the effectiveness of SEM autofocus algorithms.
- Further research into advanced autofocus strategies can improve SEM automation efficiency.
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