集成的极化,距离和旋转用于多DoF衍射处理器和信息加密信息加密.
Teng Zhang1, Xiaofei Zang1,2, Zhiyu Tan1
1Terahertz Technology Innovation Research Institute, and Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Advanced materials (Deerfield Beach, Fla.)
|June 13, 2025
概括
这项研究介绍了一种使用元表面的多自由度衍射深度神经网络 (D2NN). 这种先进的光学计算方法增强了人工智能任务和安全通信的信息处理.
科学领域:
- 光学计算是指光学计算
- 人工智能的人工智能是人工智能.
- 地表表面技术的技术.
背景情况:
- 全光衍射深度神经网络 (D2NN) 对于高速,低功耗光学计算和人工智能至关重要.
- 将多个自由度 (多个DoF) 集成到D2NN中对于增强信息处理至关重要,但也带来了重大挑战.
研究的目的:
- 提出并实验证明一个多DoF衍射处理器.
- 集成两极化,距离和旋转通道,用于多功能任务和信息加密.
主要方法:
- 采用基于超表面的方法来创建一个三层衍射处理器.
- 该处理器的设计是为了将偏振,距离和旋转作为独立的通道集成在一起.
主要成果:
- 多DoF D2NN成功执行了各种任务,包括数字分类,逻辑操作和图像转换.
- 通过将数据映射到多个DoF通道并使用莫尔斯码编码来实现一个高安全性的信息传输系统.
- 该系统表现出高度的任务能力和多功能性.
结论:
- 在全光学衍射处理器中集成多个自由度 (偏振,距离,旋转) 是可行的和有益的.
- 这种方法使多功能集成设备和先进的通信系统成为可能,为下一代光学计算铺平了道路.
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