@inproceedings{d3a278d3733b4addab77d56ccc5f6185,
title = "A physics-driven neural network framework for end-to-end inverse design of metasurface-based holograms",
abstract = "A novel unsupervised deep neural network framework driven by a physics model is introduced to design metasurface-based holograms. The proposed framework shows perfect reconstructions of holographic images with a shorter prediction time, higher peak signal-to-noise ratio and better structural similarity compared with the conventional Gerchberg-Saxton algorithm. An end-to-end design of metasurface-based holograms without requirements of complete light modulation is demonstrated. The proposed framework opens up a new approach to inverse design of metasurface-based photonic devices.",
author = "Wei Wei and Ping Tang and Jingzhu Shao and Jiang Zhu and Xiangyu Zhao and Chongzhao Wu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz 2023 ; Conference date: 17-09-2023 Through 22-09-2023",
year = "2023",
doi = "10.1109/IRMMW-THz57677.2023.10299251",
language = "English",
series = "International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz",
publisher = "IEEE Computer Society",
booktitle = "IRMMW-THz 2023 - 48th Conference on Infrared, Millimeter, and Terahertz Waves",
address = "United States",
}