发表机构
Nanjing University of Posts and Telecommunications（南京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出矢量衍射深度神经网络（V-D2NN），将全矢量角谱法嵌入端到端训练流程，在偏振复用任务及纵向场工程中性能优于传统模型，还提供开源训练框架支持相关领域发展。
AI 中文摘要
传统衍射深度神经网络（D2NN）将偏振视为独立通道，依赖标量或半矢量传播模型，忽略了交叉偏振耦合和矢量衍射。本文提出一种矢量D2NN（V-D2NN），将全矢量角谱法嵌入端到端训练流程。该物理严谨的框架描述了级联衍射层间的矢量光与物质相互作用，无需外部偏振光学器件即可直接优化偏振转换、自旋轨道耦合和矢量干涉。我们在偏振复用任务（矢量光束生成、偏振相关成像与分类、多通道光学加密）中验证了V-D2NN，其性能始终优于标量和半矢量同类模型。在纵向场工程中，V-D2NN可主动塑造指定纵向场，归一化相关系数达0.839，这是标量传播模型无法实现的能力。本文还提供了开源训练框架，以支持矢量衍射光学在计算、传感和通信领域的进一步发展。
英文摘要
Conventional diffractive deep neural networks (D2NNs) treat polarization as independent channels and rely on scalar or semi-vectorial propagation models, which neglect cross polarization coupling and vector diffraction. Here we propose a vector D2NN (V-D2NN) that embeds the full vector angular spectrum method into the end-to-end training pipeline. This physically rigorous framework describes vectorial light-matter interactions across cascaded diffractive layers, enabling direct optimization of polarization conversion, spin-orbit coupling, and vectorial interference without external polarization optics. We demonstrate the V-D2NN on polarization-multiplexed tasks--ector beam generation, polarization dependent imaging and classification, and multiple channel optical encryption--where it consistently outperforms scalar and semi-vectorial counterparts. In longitudinal-field engineering, the V-D2NN actively shapes a prescribed longitudinal field with a normalized correlation of 0.839, a capability inaccessible to scalar-propagation models. An open-source training framework is also provided to support further development of vectorial diffractive optics for computing, sensing, and communications.
Comments15 pages, 7 figures
