Deng Yang, Ren Simiao, Fan Kebin, Malof Jordan M, Padilla Willie J
Opt Express. 2021 Mar 1;29(5):7526-7534. doi: 10.1364/OE.419138.
All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently uses relatively simple unit-cell designs, but increased geometrical complexity may yield even greater scattering states. Although machine learning has recently been applied to the design of metasurfaces with impressive results, the much more challenging task of finding a geometry that yields a desired spectra remains largely unsolved. We propose and demonstrate a method capable of finding accurate solutions to ill-posed inverse problems, where the conditions of existence and uniqueness are violated. A specific example of finding the metasurface geometry which yields a radiant exitance matching the external quantum efficiency of gallium antimonide is demonstrated. We also show how the neural-adjoint method can intelligently grow the design search space to include designs that increasingly and accurately approximate the desired scattering response. The neural-adjoint method is not restricted to the case demonstrated and may be applied to plasmonics, photonic crystal, and other artificial electromagnetic materials.
全介质超表面展现出奇异的电磁响应，类似于基于金属的超材料所获得的响应。目前全介质超表面的研究使用相对简单的单元结构设计，但增加几何复杂性可能会产生更丰富的散射状态。尽管机器学习最近已应用于超表面设计并取得了令人印象深刻的成果，但找到能产生所需光谱的几何结构这一更具挑战性的任务在很大程度上仍未解决。我们提出并演示了一种能够找到不适定逆问题精确解的方法，这类问题存在性和唯一性条件被违反。展示了一个具体例子，即找到能产生与锑化镓外量子效率相匹配的辐射出射度的超表面几何结构。我们还展示了神经伴随方法如何智能地扩展设计搜索空间，以纳入越来越精确逼近所需散射响应的设计。神经伴随方法不限于所展示的情况，可应用于等离子体学、光子晶体及其他人工电磁材料。
