Inverse Design of Manufacturable Infrared Metasurfaces Based on Multimodal Deep Learning Methods

Neural networks have emerged as an effective method for inverse design of metasurfaces. Despite significant progress in inverse design for photonic structures, the inherent complexity from high-dimensional parameter spaces and the nonlinear mapping between structural parameters and optical responses still pose major challenges for the on-demand design of complex photonic systems. In this paper, we propose a multimodal neural network framework for the inverse design of composite periodic microstructures. The proposed framework can generate design results for different modes based on the target spectrum, offering flexibility in meeting design requirements, which solves the inverse design problem efficiently, achieving speeds several orders of magnitude faster than traditional methods. Furthermore, given the critical importance of precise infrared emissivity control in stealth applications, we designed infrared stealth metasurfaces capable of radiative heat dissipation through nonatmospheric windows using the well-trained network. Subsequently, the sample was fabricated for experimental validation. The results demonstrate that, while preserving the low emissivity in the atmospheric window, the average IR emissivity of our prepared samples achieves 0.674 in the 5-8 mu m nonatmospheric window. This methodology achieves radiative heat dissipation that is compatible with infrared stealth. This paper gives a novel notion for the inverse design of complicated photonic devices, which has a broad application value.