Mode decomposition is a powerful tool for analyzing the modal content of optical multimode radiation. There are several basic principles on which this tool can be implemented, including near-field intensity analysis, machine learning, and spatial correlation filtering (SCF). The latter is meant to. With the success of deep neural networks (DNNs), AI-driven mode decomposition (MD) has emerged as a leading solution for MMFs. Additionally, achieving the. Chenxin Gao, Chengjiu Wang, Zhenghao Jiao, Bo Cao, Xiaosheng Xiao, Changxi Yang, and Chengying Bao,†State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, China. With the commercialization of liquid crystal devices, digital holography as an enabling tool has be-come accessible to all, and with it all-digital tools for the decompo-sition of light has finally. Acquiring precise information about the mode content of a laser is critical for multiplexed optical communications, optical imaging with active wave-front control, and quantum-limited interferometric measurements.
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