Random broadband filters based on combination of metasurface and multilayer thin films for hyperspectral imaging

Metasurface based micro-spectrometer presents a promising avenue for achieving compact, lightweight, and cost-effective solutions for miniaturization of hyperspectral imaging systems. Nevertheless, this type of design encounter limitations primarily due to constrained manipulation mechanism of light field, resulting in high cross-correlation among transmission spectra and imperfect reconstructed images. In this paper, we propose and numerically demonstrate a micro-spectrometer based on metasurface combined with multilayer thin films, whose spectral response improves performance for application, i.e. achieving low spectral cross-correlation. Additionally, we incorporate particle swarm optimization with compressed sensing algorithm to optimize the proposed micro-spectrometer. This approach effectively reconstructs both narrowband and broadband hyperspectral signals with minimal error, achieving an impressive 2nm spectral resolution. The simulation results of hyperspectral imaging demonstrated that the proposed methodology successfully reconstructs broadband hyperspectral images with an average spectral fidelity of 91.42%. This method holds significant potential for integrating into smartphones and other portable spectrometers, advancing the design of compact hyperspectral imaging systems.