发表机构
Chalmers University of Technology; King’s College London（查尔姆斯理工大学; 伦敦国王学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出物理定制的深度学习代理模型，利用电磁局域性与对称性，将逆向设计扩展到大规模自由形态超表面，实现快四个数量级的场预测，并实验验证了75度衍射角超光栅及16亿自由度全息超表面。
AI 中文摘要
超表面是一种超薄光学元件，通过精心设计的、尺寸小于光波长的结构来控制光，从而能够实现具有传统光学难以实现的功能的紧凑器件。然而，在大面积上充分利用其全部设计自由度一直受到逆向设计所需的重复电磁模拟的巨大计算成本的阻碍。在此，我们引入一种物理定制的深度学习代理模型，该模型利用电磁相互作用的局域性和对称性，将从小规模模拟推广到规模大数个数量级的超表面。该模型在捕捉非局域相互作用的同时，预测光学场的速度比其训练所基于的传统电磁求解器快四个数量级以上，从而能够在任意照明下进行基于梯度的拓扑优化。我们通过逆向设计和实验实现衍射角高达75度的自由形态超光栅，以及一个跨越超过1000个波长、包含16亿个自由度的毫米级自由形态全息超表面，验证了该方法的有效性。通过将电磁模拟的规模与逆向设计的规模解耦，我们的方法能够实现保留纳米级设计自由度的系统级自由形态光子器件。
英文摘要
Metasurfaces are ultrathin optical elements that control light through carefully engineered structures smaller than the wavelength of light, enabling compact devices with functionalities that are difficult to achieve with conventional optics. However, exploiting their full design freedom over large areas has been prohibited by the large computational cost of repeated electromagnetic simulations required for inverse design. Here we introduce a physics-tailored deep-learning surrogate model that exploits the locality and symmetries of electromagnetic interactions to generalize from small-scale simulations to metasurfaces orders of magnitude larger. The model captures nonlocal interactions while predicting optical fields more than four orders of magnitude faster than the conventional electromagnetic solver it is trained on, enabling gradient-based topology optimization under arbitrary illumination. We validate the approach through the inverse design and experimental realization of free-form metagratings with diffraction angles up to 75 degrees, as well as a millimeter-scale free-form holographic metasurface more than 1,000 wavelengths across and comprising 1.6 billion degrees of freedom. By decoupling the scale of electromagnetic simulation from the scale of inverse design, our approach enables system-scale free-form photonic devices that retain nanoscale design freedom.
Comments27 pages, 9 figures
