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.

本文介绍一种基于神经 - 权值方法的深度学习方案，用于更加精确高效的设计以实现目标频率依赖散射效果的增加几何复杂度的全电介质超表面，并且证明该方法不仅适用于本文中的案例，同时适用于等离子体学、光子晶体带隙材料和其他结构化材料系统。