Deep-learning assisted fast orbital angular momentum complex spectrum analysis
Shiyun Zhou, Lang Li, Chunqing Gao, Shiyao Fu
Abstract
Analyzing the orbital angular momentum (OAM) distribution of a vortex beam is critical for OAM-based applications. Here, we propose a deep residual network (DRN) to model the relationship between characteristics of the multiplexed OAM beam and their complex spectrum. The favorable experimental results show that our proposal can obtain both the intensity and phase terms of multiplexed OAM beams, dubbed complex spectrum, with a wide range of OAM modes, varying in intensity, phase ratio, and mode intervals at high accuracy and real-time speed. Specifically, the root mean square error (RMSE) of intensity and phase spectrum is evaluated as 0.002 and 0.016, respectively, with a response time of only 0.020 s. To the best of our knowledge, this work opens a new sight for fast OAM complex spectrum analysis and paves the way for numerous advanced domains that need real-time OAM complex spectrum diagnostic like ultrahigh-dimensional OAM tailoring.