Angle-insensitive meta-surface color filters designed by integrating genetic algorithm with artificial neural network

Next-generation displays require tiny pixel sizes and are applied to AR/VR and holograms. Conventional color filters have limitations in miniaturization and shows poor reliability from heat, light and other environment. Meta-surface color filters (MCFs) are one of the solutions to replace conventional color filters, suitable for high-resolution displays. Structural design parameters are adjusted to determine the filtering properties of the MCFs. In addition, the metal based meta-surface shows better durability cause of the intrinsic characteristics of the material. However, if the structure is complicated, there require many case-studies for verification, which increases trial and error. In addition, resonance by phase matching between structures changes according to the angle of incident light. In this study, we suggest a method to reduce the trial and error required in designing structure. Also, the design parameters of the MCFs of the desired color was extracted through an AI model capable of reverse design. Furthermore, we propose a novel MCFs that shows characteristics independent to the angle of incident light through a coaxial hole and disk array structure.