Metasurface-Based Intelligent Identification of Total Angular Momentum Spectra for Beams
Lang Li, Liliang Gao, Yuxin Cheng, Shiyun Zhou, Jiaqi Wang, Haoran Yu, Chunqing Gao, Shiyao Fu
Abstract
The total angular momentum (TAM), consisting of spin angular momentum (SAM) and orbital angular momentum (OAM), is a crucial indicator for characterizing the topological features of structured beams. However, current diagnostic methods have limited measurable modes, making it difficult to obtain the TAM spectrum. Here, we present a metasurface-based intelligent scheme for measuring the TAM spectrum. We designed and fabricated a metasurface to transform the TAM modes into Hermite–Gaussian-like modes for simplifying judgment and developed a deep learning network, whose core stages are several mobile inverted bottleneck convolution layers for mode decomposition, for accurate TAM spectrum identification. The favorable experimental results demonstrate that our proposal can precisely measure structured beams carrying up to 34 TAM modes. Furthermore, robustness tests of this proposal under noise, angular shift, and transverse rotation demonstrate that our model is capable of accurate performance in the presence of these adverse effects within a certain range. This work presents a new path for measuring the TAM spectrum in a miniaturized form, with high accuracy, simple operation, and wide measurable modes range, which will inspire more cutting-edge scenarios such as laser communication, high security holographic encryption, and quantum information processing.