多路复用
全息术
计算机科学
光子学
波分复用
可扩展性
电子工程
光学性能监测
极限(数学)
光传送网
串扰
人工神经网络
光学
高保真
光学计算
光通信
光纤
材料科学
光开关
忠诚
无源光网络
物理
简单(哲学)
编码(内存)
拓扑(电路)
三维光学数据存储
作者
Zhiyu Tan,Xiaofei Zang,Zhe Gao,Jianshu Miao,Alexander P. Shkurinov,Fei Ding,Yiming Zhu,Songlin Zhuang
摘要
ABSTRACT High‐capacity optical multiplexing is crucial for next‐generation intelligent information systems, spanning high‐speed communications, optical computing, and immersive displays. A fundamental challenge is to maximize the number of independent channels while maintaining high fidelity, compact footprint, and low structural complexity, constraints that severely limit existing diffractive optical elements. Here, we overcome these long‐standing trade‐offs by introducing a multi‐dimensional reconfigurable diffractive neural network (MRDNN) that enables high‐capacity, high‐fidelity optical multiplexing within an exceptionally compact and simple architecture through the co‐design of predictive neural optimization and multi‐physical‐dimension multiplexing. Experimentally, a dual‐layer MRDNN realizes 24 independent channels for distinct functionalities, including focusing, beam steering, and holographic image reconstruction. Owing to its excellent scalability, only four metasurface layers comprising a minimal library of 200 2 meta‐atoms achieve 120 distinct optical channels with low crosstalk and high fidelity (measured mean structural similarity index measure of over 0.8), representing a record experimental multiplexing capacity with one of the smallest normalized footprints and simplest structural configurations in a metasurface‐enabled system. This work establishes a scalable and efficient paradigm for extreme optical multiplexing in multifunctional and reconfigurable photonics systems.
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