Improving holographic reconstruction by automatic Butterworth filtering for microelectromechanical systems...

Area of Science:

• Optical metrology
• Holographic characterization
• Surface analysis

Background:

• Digital holographic microscopy (DHM) is crucial for microscale surface investigation.
• Microelectromechanical systems (MEMS) analysis demands high accuracy due to surface roughness and complex geometries.
• Existing DHM methods face challenges with high-roughness surfaces.

Purpose of the Study:

• To develop an automatic spectral filtering procedure for DHM.
• To improve the quality and accuracy of reconstructed images for MEMS inspection.
• To enable real-time quantitative phase imaging for MEMS analysis.

Main Methods:

• Implementation of an automatic spectral filtering procedure.
• Utilizing Butterworth filtering for noise reduction in holographic reconstructions.
• Application of the method to high-roughness MEMS surfaces.

Main Results:

• Significant reduction in noise for reconstructed DHM images.
• Demonstrated increase in image quality and measurement accuracy.
• Validation of the technique's effectiveness on rough surfaces.

Conclusions:

• The proposed automatic Butterworth filtering is highly effective for MEMS analysis.
• The tunable and automatic nature makes it suitable for real-time applications.
• This method enhances quantitative phase imaging capabilities in optical metrology.