Deep learning-assisted inverse design of metasurfaces for active color image tuning

Metasurfaces, artificial planar nanostructures, offer numerous advantages for color printing applications, including ultra-high resolution, resistance to fading, wide color gamut coverage, and multifunctional capabilities. Due to the sensitivity of their resonance spectra to the external environment, metasurfaces have garnered significant interest for color tuning applications. However, most existing approaches are limited to passive color tuning, wherein only the color changes passively while the composite color image remains unaltered. Active color image tuning, on the other hand, requires precise matching of both color and intensity to the designed targets before and after the tuning process. In this study, we propose a novel approach for active metasurface color image tuning by modulating the environmental refractive index. Building upon a forward neural network that establishes the relationship between the metasurface geometric parameters and color/intensity information, we employ a multi-objective inverse adjoint neural network. This network not only overcomes the inherent 'one-to-many' problem in inverse design using neural networks but also facilitates active color image tuning under three distinct environmental conditions. Our work provides a new approach for the inverse design of metasurfaces and opens up possibilities for applications in dynamic color printing, information encryption, and other related fields.