Metrics for focusing in extremely noisy scanning electron microscopy condition

Area of Science:

• Materials Science
• Microscopy
• Image Analysis

Background:

• Accurate image focusing is critical for Scanning Electron Microscopy (SEM) but challenging under noisy conditions.
• Existing methods often struggle to achieve optimal focus, especially with low signal-to-noise ratios.
• High-performance automated focusing is needed to enhance SEM usability and application scope.

Purpose of the Study:

• To develop a robust method for automatically selecting the best focused SEM image.
• To achieve focusing performance superior to human experts in extremely noisy environments.
• To quantitatively evaluate and improve the noise immunity of the proposed focusing technique.

Main Methods:

• Utilized a specialized scanning method to acquire a series of SEM images with varying objective lens focus.
• Employed a covariance metric derived from the acquired image series to identify the optimal focus.
• Quantitatively assessed the noise immunity of the covariance-based focusing approach.

Main Results:

• The covariance metric effectively selected the best focused image from a series under highly noisy SEM conditions.
• The developed method demonstrated superior focusing performance compared to expert operators.
• Noise immunity was evaluated and subsequently enhanced, improving the method's reliability.

Conclusions:

• The covariance metric offers a powerful and noise-resilient solution for automated SEM image focusing.
• This technique significantly improves the ability to obtain high-quality images in challenging, noisy environments.
• The enhanced method holds potential for broad applications in computer-controlled SEM systems.