High-Speed Design of Multiplexed Meta-Optics Enabled by Physics-Driven Self-Supervised Network.

Abstract

The artificial intelligence (AI) can accelerate the meta-optics design by rapidly predicting the transmission coefficients of individual meta-atoms. However, extensive optimization iterations are usually required to complete the desired metasurface consisting of massive meta-atoms. For designing meta-holography, any change to the target image forces the whole process to repeat, resulting in lengthy computation time. Here, a physics-driven self-supervised network (PDSS-Net) built upon AI-assisted optimization frameworks are proposed to further expedite the design process. The encoder-decoder module introduced into the PDSS-Net can establish a mapping between the input holographic images and the output structural parameters of all meta-atoms. After self-supervised training, the network learns this mapping and enables iteration-free inference for inputs beyond the training dataset. The design of 2K-resolution, three-wavelength-multiplexed meta-holograms is completed within one second, achieving a computational speedup exceeding 1000-fold over conventional optimization-based approaches. By retraining, more complex tasks are achieved as demonstrated in the design of both the wavelength-polarization-depth multiplexed scalar and vectorial meta-holograms. This iteration-free computational paradigm with adaptability in typical multiplexed meta-optics can be applied to the intelligent design of multifunctional metasurfaces, facilitating large-scale applications of meta-devices.