用AI替代传统试错，高效设计多功能纳米光子器件。
Data driven approaches in nanophotonics: A review of AI-enabled metadevices
- 用深度学习替代电磁仿真，快速探索复杂设计空间。
- 支持高自由度设计与大语言模型辅助，提升设计效率。
- 适合从事纳米光子器件研发的工程师和研究者。
数据驱动方法通过先进的人工智能技术，彻底改变了光子超材料器件的设计与优化。本文从模型视角出发，综合新兴设计策略，阐明传统试错法和计算密集型电磁仿真正被深度学习框架取代，从而高效探索大规模设计空间。文章讨论了人工智能在高自由度设计、大语言模型辅助设计等多个超材料设计环节的应用。针对变换器模型实现、制造限制及复杂耦合效应等挑战，这些AI驱动策略不仅简化了正向建模过程，还为实现多功能且可制造的纳米光子器件提供了稳健路径。本文进一步揭示了新兴机遇与持续挑战，为下一代纳米光子工程策略奠定基础。
原文摘要 · Abstract (English)
Data-driven approaches have revolutionized the design and optimization of photonic metadevices by harnessing advanced artificial intelligence methodologies. This review takes a model-centric perspective that synthesizes emerging design strategies and delineates how traditional trial-and-error and computationally intensive electromagnetic simulations are being supplanted by deep learning frameworks that efficiently navigate expansive design spaces. We discuss artificial intelligence implementation in several metamaterial design aspects from high-degree-of-freedom design to large language model-assisted design. By addressing challenges such as transformer model implementation, fabrication limitations, and intricate mutual coupling effects, these AI-enabled strategies not only streamline the forward modeling process but also offer robust pathways for the realization of multifunctional and fabrication-friendly nanophotonic devices. This review further highlights emerging opportunities and persistent challenges, setting the stage for next-generation strategies in nanophotonic engineering.
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