In this paper, we present a novel approach for the design of leaky-wave holographic antennas that generates OAM-carrying electromagnetic waves by combining Flat Optics (FO) and machine learning (ML) techniques. To improve the performance of our system, we use a machine learning technique to discover a mathematical function that can effectively control the entire radiation pattern, i.e., decrease the side lobe level (SLL) while simultaneously increasing the central null depth of the radiation pattern. Precise tuning of the parameters of the impedance equation based on holographic theory is necessary to achieve optimal results in a variety of scenarios. In this research, we applied machine learning to determine the approximate values of the parameters. We can determine the optimal values for each parameter, resulting in the desired radiation pattern, using a total of 77,000 generated datasets. Furthermore, the use of ML not only saves time, but also yields more precise and accurate results than manual parameter tuning and conventional optimization methods.

翻译：本文提出了一种结合平面光学(FO)与机器学习(ML)技术的漏波全息天线设计新方法，用于产生携带轨道角动量(OAM)的电磁波。为提升系统性能，我们采用机器学习技术探索能有效控制整个辐射图样的数学函数，即在降低旁瓣电平(SLL)的同时增大辐射图样中心零陷深度。基于全息理论的阻抗方程参数精确调谐对实现多场景最优结果至关重要。本研究通过机器学习确定参数的近似值，利用总计77,000组生成数据集，可获取各参数的最优值以生成所需辐射图样。与人工参数调谐及传统优化方法相比，机器学习不仅节省时间，还能获得更精确、更准确的结果。