Metasurface holography has aroused immense interest in producing holographic images with high quality, higher-order diffraction-free, and large viewing angles by using a planar artificial sheet consisting of subwavelength nanostructures. Despite remarkable progress, dynamically tunable metasurface holography in the visible band has rarely been reported due to limited available tuning methods. In this work, we propose and numerically demonstrate a thermally tunable vanadium dioxide (VO2) nanofin based binary-phase metasurface, which generates holographic information in the visible varying with temperature. The insulator-to-metal phase transition in VO2 nanofins allows two independent binary-phase holograms generated by machine learning to be encoded in the respective phases of VO2 and switched under thermal regulation. By elaborately designing the dimensions and compensated phase of VO2 nanofins, high-quality images are reconstructed at corresponding temperatures under appropriate chiral illumination. In contrast, much poorer images are produced under inappropriate chiral illumination. We further demonstrate the advantage of applying the VO2 phase-compensated metasurface in high-security digital encryption, where two desired character combinations are read out with appropriate excitations and temperatures, whereas one identical fraudulent message is received with inappropriate excitations. Our design approach offers a new and efficient method to realize tunable metasurfaces, which is promisingly adopted in dynamic display, information encryption, optical anti-counterfeiting, etc.
Keywords: information encryption; machine learning optimization; metasurface holography; tunable metasurface; vanadium dioxide.
© 2024 the author(s), published by De Gruyter, Berlin/Boston.