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, a mini‐spectrometer that combines a large‐scale radiative cavity array with end‐to‐end deep learning networks is proposed and demonstrated. Specifically, high‐ Q bound states are utilized in continuum cavities with distinct radiation characteristics as the fundamental units to achieve parallel spectral detection. An array of resonators with quality factors above 10 ⁴ over a wide spectral range, from 1525 to 1605 nm, is realized. A deep network with 8000 outputs is further trained 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.