Work

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

Year: 2025

Type: article

Abstract: To mitigate the influence of phase-shift errors in wavelength-tuning phase-shifting interferometry, it is essential to enhance the precision of the phase-shifting steps and develop algorithms that are less sensitive to such errors. Leveraging the robust recognition capabilities of neural networks, we propose an Artificial Neural Network Phase-shifting Algorithm (ANNPA) for phase-shifting interferometry, detailing the network's design and training methods while optimizing calculation steps. By specially designing the training dataset, we fundamentally suppress high-order phase-shift errors. Simulations validate the algorithm's excellent insensitivity to both linear phase-shift errors and random noise in phase-shifting. Finally, we conducted a phase-shifting interferometry experiment using a Fizeau interferometer and employed ANNPA for calculations, with results clearly demonstrating its ability to perform multi-surface phase-shifting interferometry under non-ideal phase-shifting conditions.

Source: Optics Express

DOI: 10.1364/oe.545872

Authors: Yuyang Tan, Shi Hu, Jinhao Wang, Ming Li, Xudong Lin

Language: English

Topic: Optical measurement and interference techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funder: National Key Research and Development Program of China

Award: G433631688