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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.12192 |
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| _version_ | 1866915866355957760 |
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| author | Jia, Zixing Li, Jiawei Chen, Ziping Li, Xin |
| author_facet | Jia, Zixing Li, Jiawei Chen, Ziping Li, Xin |
| contents | We propose a multimodal fusion network (MFN) for precise micro-displacement measurement using a modified Michelson interferometer. The model resolves the intrinsic half-wave displacement ambiguity that limits conventional single-wavelength interferometry by introducing a dual-head learning mechanism: one head performs sub-half-wave displacement regression, and the other classifies integer interference orders. Unlike dual-wavelength or iterative fitting methods, which require high signal quality and long computation time, MFN achieves robust, real-time prediction directly from interferometric images.
Trained on 2x10^5 simulated interferograms and fine-tuned with only about 0.24% of real experimental data (about 500 images), the model attains a displacement precision of 4.84(15) nm and an order-classification accuracy of 98%. Even under severe noise, MFN maintains stable accuracy (about 16 nm RMSE), whereas conventional heuristic algorithms exhibit errors exceeding 100 nm. These results demonstrate that MFN offers a fast, noise-tolerant, and cost-efficient solution for single-wavelength interferometric metrology, eliminating the need for multi-wavelength hardware or complex phase fitting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Fusion Network for Micro-displacement Measurement via Michelson Interferometer Jia, Zixing Li, Jiawei Chen, Ziping Li, Xin Optics We propose a multimodal fusion network (MFN) for precise micro-displacement measurement using a modified Michelson interferometer. The model resolves the intrinsic half-wave displacement ambiguity that limits conventional single-wavelength interferometry by introducing a dual-head learning mechanism: one head performs sub-half-wave displacement regression, and the other classifies integer interference orders. Unlike dual-wavelength or iterative fitting methods, which require high signal quality and long computation time, MFN achieves robust, real-time prediction directly from interferometric images. Trained on 2x10^5 simulated interferograms and fine-tuned with only about 0.24% of real experimental data (about 500 images), the model attains a displacement precision of 4.84(15) nm and an order-classification accuracy of 98%. Even under severe noise, MFN maintains stable accuracy (about 16 nm RMSE), whereas conventional heuristic algorithms exhibit errors exceeding 100 nm. These results demonstrate that MFN offers a fast, noise-tolerant, and cost-efficient solution for single-wavelength interferometric metrology, eliminating the need for multi-wavelength hardware or complex phase fitting. |
| title | Multimodal Fusion Network for Micro-displacement Measurement via Michelson Interferometer |
| topic | Optics |
| url | https://arxiv.org/abs/2511.12192 |