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Updated: Jul 11, 2025

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

Published on: February 27, 2013

Deep learning for sub-Nyquist sampling scanning white light interferometry.

Hangang Liang, Honghai Shen, Penghui Liu

|November 15, 2023

Summary

This summary is machine-generated.

This study introduces a deep learning method for sub-Nyquist sampling vertical scanning white light interferometry (SWLI). The novel approach enhances measurement speed and accuracy without requiring prior knowledge.

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Last Updated: Jul 11, 2025

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

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Area of Science:

Optics and Photonics
Artificial Intelligence
Signal Processing

Background:

Vertical scanning white light interferometry (SWLI) is a crucial technique for high-resolution surface profiling.
Traditional SWLI requires high sampling rates, leading to large data volumes and slow acquisition.
Sub-Nyquist sampling offers a path to faster measurements but often compromises accuracy.

Purpose of the Study:

To develop a deep learning-based method for sub-Nyquist sampling in SWLI.
To extract accurate surface profiles from undersampled interferometric signals.
To improve the efficiency, accuracy, and robustness of SWLI measurements.

Main Methods:

Introduction of Envelope-Deep Residual Shrinkage Networks with channel-wise thresholds (E-DRSN-cw).
Extraction of oversampling envelopes from undersampled signals using a deep learning model.
Implementation of soft thresholding, zero-padding, Layer Normalization, and regression envelope prediction for enhanced training.

Main Results:

The E-DRSN-cw model demonstrated superior performance compared to classical methods in simulations and experiments.
Achieved significant improvements in accuracy and robustness for SWLI measurements.
Validated the effectiveness of the deep learning approach for processing undersampled interferometric data.

Conclusions:

The E-DRSN-cw method provides a swift and accurate measurement solution for SWLI.
This deep learning approach eliminates the need for prior knowledge about the signal or system.
Enables efficient high-resolution surface profiling with reduced data acquisition requirements.