Review of Applications of Machine Learning in Optoelectronic Information Science
Main Article Content
Keywords
machine learning, optoelectronic information science, optoelectronic materials, optical communication, photonic devices
Abstract
With the rapid advancement of artificial intelligence technology, machine learning—as one of its core branches—has demonstrated immense application value in the field of optoelectronic information science. This article systematically reviews the research progress on the practical applications of machine learning in several sub-fields of optoelectronic information science. It covers core areas such as the design and optimization of optoelectronic materials, photoelectric detection and imaging, optical communication systems, photonic device design, and photoelectron spectroscopy analysis. It also analyzes the major challenges faced in the current application process, such as data quality and model interpretability. Future research directions are outlined, providing a systematic reference basis for researchers in related fields.
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