基于深度学习的微纳结构光谱设计研究进展

Research Progress on Deep Learning-Based Spectral Design of Micro-Nano Structures

  • 摘要: 随着人工智能技术的快速发展,深度学习在微纳结构光谱调控领域展现出了巨大的应用潜力。深度学习可以在无明确物理解析模型的情况下,通过构建复杂的神经网络,从实验或仿真数据中学习微纳结构的光谱响应特性,从而实现高效的设计优化,这为微纳结构的设计提供了一种新的思路和方法。该文综述了近年来深度学习在微纳结构设计中的研究进展,重点讨论了其在结构色、热辐射控制以及窄带光谱传感等光谱调控领域的应用,并展望了该领域未来的发展机遇与挑战。

     

    Abstract: With the rapid development of artificial intelligence technology, deep learning has shown tremendous potential in the field of spectral regulation of micro-nano structures. By constructing complex neural network models, deep learning can learn the spectral response characteristics of micro-nano structures from experimental or simulation data without the need for explicit physical analytical models, thereby achieving efficient design optimization. This provides a new approach and methodology for the design of micro-nano structures. This paper reviews the recent research progress of deep learning in micro-nano structure design, focusing on its applications in structural color, thermal radiation control, and narrowband spectral sensing, and also discusses future opportunities and challenges in this field.

     

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