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Performance Analysis of Surface Reconstruction Algorithms in Vertical Scanning Interferometry Based on Coherence

Dongxu Wu^1,2, Fusheng Liang¹, Chengwei Kang¹

¹Centre of Micro/Nano Manufacturing Technology (MNMT-Dublin), University College Dublin, Dublin 4, Ireland.

|February 11, 2021

Summary

This study compares coherence envelope detection algorithms for optical interferometry. The Hilbert transform (HT) offers superior speed and accuracy over centroid and Fast Fourier Transform (FFT) methods, especially in noisy environments.

Keywords:

algorithm envelope detection height measurement low coherence surface topography vertical scanning interferometry

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Area of Science:

Precision Metrology
Optical Engineering
Surface Characterization

Background:

Optical interferometry is crucial for high-precision surface measurements.
Accurate surface reconstruction from interferograms depends on robust algorithms.
Environmental noise and system disturbances degrade algorithm performance.

Purpose of the Study:

To comparatively analyze three coherence envelope detection algorithms: centroid, Fast Fourier Transform (FFT), and Hilbert Transform (HT).
To evaluate algorithm performance in vertical scanning interferometry regarding accuracy, speed, and noise resistance.
To identify the most suitable algorithm for reliable topographical surface measurement.

Main Methods:

Numerical simulations and experimental validation using a custom interferometer.
Measurement of step height standards to reconstruct surface profiles.
Comparative performance analysis of centroid, FFT, and HT algorithms.

Main Results:

Centroid method offers high speed but limited accuracy in noisy conditions.
FFT and HT methods demonstrate superior noise immunity and measurement accuracy.
HT method provides comparable accuracy to FFT but with significantly faster processing speeds.

Conclusions:

The Hilbert Transform (HT) is recommended for coherence envelope detection in vertical scanning interferometry due to its balance of speed and accuracy.
FFT and HT are more robust against noise than the centroid method for topographical surface measurement.
Algorithm selection significantly impacts the reliability of precision surface characterization.