Miniaturized spectrometer enabled by end-to-end deep learning on large-scale radiative cavity array
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912127878430720 |
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| author | Zhou, Xinyi Zhang, Cheng Zhang, Xiaoyu Zuo, Yi Zhang, Zixuan Wang, Feifan Chen, Zihao Li, Hongbin Peng, Chao |
| author_facet | Zhou, Xinyi Zhang, Cheng Zhang, Xiaoyu Zuo, Yi Zhang, Zixuan Wang, Feifan Chen, Zihao Li, Hongbin Peng, Chao |
| contents | Miniaturized (mini-) spectrometers are highly desirable tools for chemical, biological, and medical diagnostics because of their potential for portable and in situ spectral detection. In this work, we propose and demonstrate a mini-spectrometer that combines a large-scale radiative cavity array with end-to-end deep learning networks. Specifically, we utilize high-Q bound states in continuum cavities with distinct radiation characteristics as the fundamental units to achieve parallel spectral detection. We realize a 36 $\times$ 30 cavity array that spans a wide spectral range from 1525 to 1605 nm with quality factors above 10^4. We further train a deep network with 8000 outputs to directly map arbitrary spectra to array responses excited by the out-of-plane incident. Experimental results demonstrate that the proposed mini-spectrometer can resolve unknown spectra with a resolution of 0.048 nm in a bandwidth of 80 nm and fidelity exceeding 95%, thus offering a promising method for compact, high resolution, and broadband spectroscopy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_13353 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Miniaturized spectrometer enabled by end-to-end deep learning on large-scale radiative cavity array Zhou, Xinyi Zhang, Cheng Zhang, Xiaoyu Zuo, Yi Zhang, Zixuan Wang, Feifan Chen, Zihao Li, Hongbin Peng, Chao Optics Miniaturized (mini-) spectrometers are highly desirable tools for chemical, biological, and medical diagnostics because of their potential for portable and in situ spectral detection. In this work, we propose and demonstrate a mini-spectrometer that combines a large-scale radiative cavity array with end-to-end deep learning networks. Specifically, we utilize high-Q bound states in continuum cavities with distinct radiation characteristics as the fundamental units to achieve parallel spectral detection. We realize a 36 $\times$ 30 cavity array that spans a wide spectral range from 1525 to 1605 nm with quality factors above 10^4. We further train a deep network with 8000 outputs to directly map arbitrary spectra to array responses excited by the out-of-plane incident. Experimental results demonstrate that the proposed mini-spectrometer can resolve unknown spectra with a resolution of 0.048 nm in a bandwidth of 80 nm and fidelity exceeding 95%, thus offering a promising method for compact, high resolution, and broadband spectroscopy. |
| title | Miniaturized spectrometer enabled by end-to-end deep learning on large-scale radiative cavity array |
| topic | Optics |
| url | https://arxiv.org/abs/2411.13353 |