Year: 2026
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Institution: Nanjing University of Posts and Telecommunications
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G255599106, G4289072425
OpenAlex ID: W7221143640