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 learn

SUBMITTER: He Y 

PROVIDER: S-EPMC12561260 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature