计算机科学
深度学习
加密
多路复用
电子工程
人工智能
电信
工程类
计算机网络
作者
Zhouyue Lei,Xu yi duo,Cheng Lei,Yan Zhao,Wei Du
出处
期刊:Photonics Research
[The Optical Society]
日期:2023-12-22
卷期号:12 (1): 123-123
摘要
Optical metasurfaces (OMs) offer unprecedented control over electromagnetic waves, enabling advanced optical multiplexing. The emergence of deep learning has opened new avenues for designing OMs. However, existing deep learning methods for OMs primarily focus on forward design, which limits their design capabilities, lacks global optimization, and relies on prior knowledge. Additionally, most OMs are static, with fixed functionalities once processed. To overcome these limitations, we propose an inverse design deep learning method for dynamic OMs. Our approach comprises a forward prediction network and an inverse retrieval network. The forward prediction network establishes a mapping between meta-unit structure parameters and reflectance spectra. The inverse retrieval network generates a library of meta-unit structure parameters based on target requirements, enabling end-to-end design of OMs. By incorporating the dynamic tunability of the phase change material Sb 2 Te 3 with inverse design deep learning, we achieve the design and verification of dynamic multifunctional OMs. Our results demonstrate OMs with multiple information channels and encryption capabilities that can realize multiple physical field optical modulation functions. When Sb 2 Te 3 is in the amorphous state, near-field nano-printing based on meta-unit amplitude modulation is achieved for X -polarized incident light, while holographic imaging based on meta-unit phase modulation is realized for circularly polarized light. In the crystalline state, the encrypted information remains secure even with the correct polarization input, achieving double encryption. This research points towards ultra-compact, high-capacity, and highly secure information storage approaches.
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