Deep learning and genetic algorithm driven accelerated design for frequency-multiplexed complex-amplitude coding meta-hologram

Frequency-multiplexed metasurfaces represent a significant innovation in breaking the functional limitations of traditional metasurfaces, showing immense potential in multi-channel communication. However, existing frequency-multiplexed metasurfaces primarily focus on pure phase and linear polarization modulation, neglecting the modulation for complex amplitude and circularly polarized waves. Additionally, crosstalk suppression between dual-frequency channels often requires meticulous tuning of the meta-atom structure. Therefore, manually designing a set of meta-atoms that satisfies both complex amplitude modulation and low crosstalk at dual frequencies is extremely challenging and time-consuming. Here, we utilize the method of deep learning and genetic algorithm to design a kind of meta-atom capable of bi-spectral 2-bit amplitude and arbitrary phase modulation, which greatly reduces the design difficulty and achieves excellent low-crosstalk performance. This method can be easily generalized to the design of other complex meta-atoms to improve the design efficiency. Furthermore, we propose a frequency-multiplexed complex-amplitude coding meta-hologram for modulating left-handed circularly polarized (LCP) waves. When illuminated with LCP light, it can reconstruct two distinct holographic images at two different frequencies in the near field with high quality. The independent modulation capability of the metasurface for multiple degrees of freedom of frequency, amplitude and phase gives it broad application prospects in multi-channel communication, data storage and perfect holography.