论文标题
神经伴侣方法用于全dilectric Metasurfaces的逆设计
Neural-adjoint method for the inverse design of all-dielectric metasurfaces
论文作者
论文摘要
全端元额叶表现出异国情调的电磁反应，类似于用金属基质材料获得的反应。目前，全端元信息的研究使用相对简单的单元设计，但几何复杂性提高可能会产生更大的散射状态。尽管最近已经将机器学习应用于具有令人印象深刻的结果的元时间的设计，但是找到产生所需光谱的几何形状的更具挑战性的任务仍然在很大程度上无法解决。我们探索并适应了一种最近的深度学习方法（称为神经间接接合），并发现它能够准确有效地估算产生靶向频率依赖性散射所需的复杂几何形状。我们还展示了如何智能地扩展设计搜索空间，以包括越来越准确地近似所需散射响应的设计。神经伴侣方法不仅限于所证明的病例，因此可以应用于血浆，光子带隙和其他结构化材料系统。
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 the desired spectra remains largely unsolved. We explore and adapt a recent deep learning approach -- termed neural-adjoint -- and find it is capable of accurately and efficiently estimating complex geometry needed to yield a targeted frequency-dependent scattering. 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 bandgap, and other structured material systems.
