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This study introduces FFARNet-18, a novel deep learning model for Michelson interferometry. It enhances physical parameter estimation accuracy by analyzing fractional-domain features from interferograms.

Area of Science:

Optics and Photonics
Artificial Intelligence
Metrology

Background:

Michelson interferometry is a high-precision, non-contact measurement technique.
Convolutional Neural Networks (CNNs) are increasingly used for automated analysis of interferograms.
Existing CNNs primarily use spatial-domain features, limiting accuracy in noisy conditions.

Purpose of the Study:

To improve the accuracy of physical parameter estimation from interferograms, especially under noise.
To develop a novel deep learning architecture that leverages both spatial and fractional-domain features.
To address data limitations in interferometric measurements through data augmentation.

Main Methods:

Proposed FFARNet-18 (Fractional Fourier Adaptive Residual Network).

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Incorporated multi-channel Fractional Fourier Transform (FRFT) branches into the CNN architecture.

Employed a geometric transformation-based data augmentation strategy.

Main Results:

FFARNet-18 achieved joint spatial-fractional domain parameter estimation.
Demonstrated substantial improvement in refractive index and thickness estimation accuracy.
Showcased enhanced model generalization capability due to data augmentation.

Conclusions:

FFARNet-18 offers superior performance over existing methods for interferogram analysis.
The integration of FRFT effectively extracts crucial fringe characteristics.
The proposed method provides a robust solution for high-accuracy metrology with minimal computational overhead.