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High-accuracy phase demodulation method compatible to closed fringes in a single-frame interferogram based on deep

Shizhu Yuan, Yao Hu, Qun Hao

|March 17, 2021

Summary

This summary is machine-generated.

This study introduces a novel neural network for accurate single-frame interferogram demodulation, even with complex closed fringes. The method achieves high precision, crucial for optical metrology applications.

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Area of Science:

Optical interferometry
Metrology
Machine learning applications

Background:

Interferogram demodulation is essential in optical interferometry.
Accurate phase retrieval from single-frame interferograms with closed fringes remains a challenge.

Purpose of the Study:

To propose a neural network architecture for single-frame interferogram demodulation.
To develop a robust training strategy using synthetic data for high-accuracy phase retrieval.

Main Methods:

A novel neural network architecture designed for interferogram demodulation.
An interferogram generation model to create a synthetic training dataset.
A four-stage training strategy with optimized loss functions and optimizers.

Main Results:

The proposed method successfully demodulates single-frame interferograms containing closed fringes.
Achieved a phase demodulation accuracy of 0.01 λ (root mean square error) on experimental data.
Demonstrated the effectiveness of the synthetic data generation and training strategy.

Conclusions:

The developed neural network provides a high-accuracy solution for single-frame interferogram demodulation.
The synthetic data generation approach overcomes limitations of real experimental data acquisition.
This method advances optical metrology by enabling precise phase analysis from complex interferograms.