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In this paper, we present a novel approach for the design of leaky-waveholographic antennas that generates OAM-carrying electromagnetic waves bycombining Flat Optics (FO) and machine learning (ML) techniques. To improve theperformance of our system, we use a machine learning technique to discover amathematical function that can effectively control the entire radiationpattern, i.e., decrease the side lobe level (SLL) while simultaneouslyincreasing the central null depth of the radiation pattern. Precise tuning ofthe parameters of the impedance equation based on holographic theory isnecessary to achieve optimal results in a variety of scenarios. In thisresearch, we applied machine learning to determine the approximate values ofthe parameters. We can determine the optimal values for each parameter,resulting in the desired radiation pattern, using a total of 77,000 generateddatasets. Furthermore, the use of ML not only saves time, but also yields moreprecise and accurate results than manual parameter tuning and conventionaloptimization methods.

Deep Learning Framework for the Design of Orbital Angular Momentum Generators Enabled by Leaky-wave Holograms