arXiv · 2411.13353
Miniaturized spectrometer enabled by end-to-end deep learning on large-scale radiative cavity array
Abstract
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.
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Xinyi Zhou, Cheng Zhang, Xiaoyu Zhang, Yi Zuo, Zixuan Zhang, Feifan Wang, Zihao Chen, Hongbin Li, Chao Peng. 2024-11-20. Miniaturized spectrometer enabled by end-to-end deep learning on large-scale radiative cavity array. https://arxiv.org/abs/2411.13353
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