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Artificial Intelligence in Nanophotonics:From Design to Optical Computing
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作者 Yongqiang Hu Jingmin Huang +14 位作者 Junyi Li Yu Sui Jiarui Zhang Tao Zheng Wenxiao Wang Chengyi Zhu Qianyi Guo Lihua Li Xilin Zhang Nihong Chen Juan Chen Yitong Chen Xiaorui Zheng Zhongmin Yang Jiewei Chen 《Chinese Physics Letters》 2025年第8期333-353,共21页
The bidirectional convergence of artificial intelligence and nanophotonics drives photonic technologies toward unprecedented levels of intelligence and efficiency,fundamentally reshaping their design paradigms and app... The bidirectional convergence of artificial intelligence and nanophotonics drives photonic technologies toward unprecedented levels of intelligence and efficiency,fundamentally reshaping their design paradigms and application boundaries.With its powerful data-driven and nonlinear optimization capabilities,artificial intelligence has become a powerful tool for optical design,enabling the inverse design of nanophotonics devices while accelerating the forward computation of electromagnetic responses.Conversely,nanophotonics provides a wave-based computational platform,giving rise to novel optical neural networks that achieve high-speed parallel computing and efficient information processing.This paper reviews the latest progress in the bidirectional field of artificial intelligence and nanophotonics,analyzes the basic principles of various applications from a universal perspective,comprehensively evaluates the advantages and limitations of different research methods,and makes a forwardlooking outlook on the bidirectional integration of artificial intelligence and nanophotonics,focusing on analyzing future development trends,potential applications,and challenges.The deep integration of artificial intelligence and nanophotonics is ushering in a new era for photonic technologies,offering unparalleled opportunities for fundamental research and industrial applications. 展开更多
关键词 optical designenabling inverse design nanophotonics devices NANOPHOTONICS photonic technologies optical design artificial intelligence design paradigms
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