Compact Snapshot Hyperspectral Imaging With Neural Dispersion‐Engineered Metalens

There is a growing demand for ultra‐compact, low‐cost, and high‐fidelity snapshot hyperspectral imaging devices across various fields. However, conventional systems struggle to fulfill all these evolving requirements. Although a single diffractive optical element offers a compact solution, its degrees of freedom in light phase modulation are limited. Here, we exploit wavelength‐dependent characteristics of the point spread function (PSF) to design a dispersion‐engineered metalens that serves as a key element for hyperspectral imaging. The PSF of the metalens is tailored to undergo an extra lateral shift as a function of wavelength, thereby encoding richer spectral information. An efficient differentiable computational model is developed to simulate the hyperspectral imaging process, together with a downstream spectral reconstruction network. The metalens structure and the reconstruction network are subsequently optimized in an end‐to‐end deep learning joint optimization framework. We fabricated the optimal metalens using two‐photon grayscale lithography and built a prototype hyperspectral imaging camera. Both indoor and outdoor experimental results validated its outstanding spatial‐spectral reconstruction performance, highlighting the effectiveness in practical applications.