发表机构
TU Dresden（德累斯顿工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种混合优化框架，结合高维数值设计与低维硬件在环校准，使可编程衍射神经网络无需重训练即可补偿实验失配，实现对六种偏振模式的空间排序，信噪比提升约3.26 dB。
AI 中文摘要
可编程衍射光学处理器在空间光操控方面特别有吸引力，因为其光学变换可以在不修改物理硬件的情况下动态重构和调整。然而，它们的实际性能常常受限于由光学像差、对准误差和非理想相位响应引起的仿真与实验之间的差距。在本文中，我们提出了一种混合优化框架，将高维数值设计与低维硬件在环校准相结合，使可编程衍射光学网络能够在实验中补偿失配，而无需重新训练其底层光学变换。我们演示了一个通过反向传播设计的可编程光学衍射神经网络（ODNN），用于对多模光纤支持的六种线性偏振模式进行空间排序。实验失真使用截断的泽尼克基表示，每层仅需19个校正系数。这些系数直接在物理系统上使用随机并行梯度下降进行优化，避免了重新优化全像素相位掩模。实验结果表明，所提出的校准方法使信噪比提高了约3.26 dB，振幅误差降低了0.04。这种将高维光学功能设计与低维物理校准分离的方法，为面向空间模式和波长的光学路由器、可编程光子计算机系统和量子信息处理的自适应可重构空间模式处理器提供了一条可扩展的路径。
英文摘要
Programmable diffractive optical processors are particularly attractive for spatial light manipulation because their optical transformations can be dynamically reconfigured and adapted without modifying the physical hardware. However, their practical performance is often limited by the gap between simulation and experiment caused by optical aberrations, alignment errors, and nonideal phase responses. In this paper, we introduce a hybrid optimization framework that combines high-dimensional numerical design with low-dimensional hardware-in-the-loop calibration, enabling programmable diffractive optical networks to compensate experimentally for mismatch without retraining their underlying optical transformations. We demonstrate a programmable optical diffractive neural network (ODNN) designed by back-propagation to spatially sort six linearly polarized modes supported by a multimode fiber. The experimental distortions are represented using a truncated Zernike basis with only 19 correction coefficients per layer. These coefficients are optimized directly on the physical system using stochastic parallel gradient descent, avoiding re-optimization of the full pixelated phase masks. Experimentally, the proposed calibration yields an SNR improvement of approximately 3.26 dB, and a decrease in amplitude error of 0.04. This separation of high-dimensional optical-function design from low-dimensional physical calibration provides a scalable route towards adaptive and reconfigurable spatial-mode processors for optical router on spatial modes and wavelengths, programmable photonic computer systems and quantum information processing.
