Hou Mengdie, Xu Mengjun, Xu Jiangtao, Lu Jiafeng, An Yi, Huang Liangjin, Zeng Xianglong, Pang Fufei, Li Jun, Yi Lilin
The Key Lab of Specialty Fiber Optics and Optical Access Network, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200444, China.
College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.
Nanophotonics. 2023 Jun 15;12(15):3165-3177. doi: 10.1515/nanoph-2023-0202. eCollection 2023 Jul.
Structured optical fields, such as cylindrical vector (CV) and orbital angular momentum (OAM) modes, have attracted considerable attention due to their polarization singularities and helical phase wavefront structure. However, one of the most critical challenges is still the intelligent generation or precise control of these modes. Here, we demonstrate the first simulation and experimental realization of decomposing the CV and OAM modes by reconstructing the multi-view images of projected intensity distribution. Assisted by the deep learning-based stochastic parallel gradient descent (SPGD) algorithm, the modal coefficients and optical field distributions can be retrieved in 1.32 s within an average error of 0.416 % showing high efficiency and accuracy. Especially, the interference pattern and quarter-wave plate are exploited to confirm the phase and distinguish elliptical or circular polarization direction, respectively. The generated donut modes are experimentally decomposed in the CV and OAM modes, where purity of CV modes reaches 99.5 %. Finally, fast switching vortex modes is achieved by electrically driving the polarization controller to deliver diverse CV modes. Our findings may provide a convenient way to characterize and deepen the understanding of CV or OAM modes in view of modal proportions, which is expected of latent applied value on information coding and quantum computation.
诸如柱矢量（CV）和轨道角动量（OAM）模式等结构化光场，因其偏振奇点和螺旋相位波前结构而备受关注。然而，最关键的挑战之一仍然是这些模式的智能生成或精确控制。在此，我们展示了通过重建投影强度分布的多视图图像来分解CV和OAM模式的首次模拟和实验实现。在基于深度学习的随机并行梯度下降（SPGD）算法的辅助下，模态系数和光场分布能够在1.32秒内以0.416%的平均误差被检索出来，显示出高效率和高精度。特别地，利用干涉图样和四分之一波片分别确认相位和区分椭圆或圆偏振方向。所产生的甜甜圈模式在实验中被分解为CV和OAM模式，其中CV模式的纯度达到99.5%。最后，通过电驱动偏振控制器来实现多种CV模式，从而实现快速切换涡旋模式。我们的研究结果可能提供一种便捷方法，从模态比例的角度来表征和加深对CV或OAM模式的理解，有望在信息编码和量子计算方面具有潜在应用价值。
