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Robust multi-surface phase-shifting interferometry based on artificial neural networks.

Yuyang Tan, Shi Hu, Jinhao Wang

|August 13, 2025

Summary

This summary is machine-generated.

This study introduces an Artificial Neural Network Phase-shifting Algorithm (ANNPA) to improve phase-shifting interferometry accuracy. ANNPA effectively reduces phase-shift errors, enabling precise measurements even with non-ideal conditions.

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Last Updated: Sep 11, 2025

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Area of Science:

Optical Metrology
Interferometry
Artificial Intelligence

Background:

Phase-shift errors limit accuracy in wavelength-tuning phase-shifting interferometry.
Developing robust algorithms is crucial for precise phase measurements.

Purpose of the Study:

To propose and validate an Artificial Neural Network Phase-shifting Algorithm (ANNPA) for enhanced phase-shifting interferometry.
To suppress high-order phase-shift errors and improve insensitivity to linear errors and noise.

Main Methods:

Designed and trained an Artificial Neural Network Phase-shifting Algorithm (ANNPA).
Optimized network design and calculation steps.
Created a specialized training dataset to suppress high-order phase-shift errors.

Main Results:

Simulations confirmed ANNPA's insensitivity to linear phase-shift errors and random noise.
Experimental validation using a Fizeau interferometer demonstrated ANNPA's effectiveness.
ANNPA successfully performed multi-surface phase-shifting interferometry under non-ideal conditions.

Conclusions:

ANNPA offers a robust solution for mitigating phase-shift errors in interferometry.
The algorithm enables accurate phase measurements in challenging, non-ideal experimental setups.