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

Abstract 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 2 nm 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.