论文标题
深度学习以加速麦克斯韦方程以进行介电元面的逆设计
Deep learning to accelerate Maxwell's equations for inverse design of dielectric metasurfaces
论文作者
论文摘要
光学元面的逆设计是一个快速新兴的领域，在微型化的传统光学器件以及开发全新的光学功能方面已经显示出巨大的希望。这样的设计过程依赖于对设备光学响应的许多前向模拟，以优化其性能。我们提供了一个数据驱动的向前仿真框架，用于跨设计的逆设计，该近相比基于本地相位近似的方法更准确，$ 10^4 $ $倍，并且比基于网状的溶解器的内存少于$ 15 $ $ $ 15 $，并且不受限制地限制在球体散射地球上。我们从波长尺度圆柱柱中探索散射的电磁场分布，通过奇异值分解获得数据的低维表示。我们创建了一个可拟合输入几何形状和元图散射器的配置的模型，以与输出字段的低维表示。为了验证我们的模型，我们逆设计两个光学元件：波长多路复用元件，该元件以$λ= 633 $ nm的焦点聚焦，并在$λ= 400 $ nm的环形束和焦点镜头的扩展深度下产生一个环形光束。
The inverse design of optical metasurfaces is a rapidly emerging field that has already shown great promise in miniaturizing conventional optics as well as developing completely new optical functionalities. Such a design process relies on many forward simulations of a device's optical response in order to optimize its performance. We present a data-driven forward simulation framework for the inverse design of metasurfaces that is more accurate than methods based on the local phase approximation, a factor of $10^4$ times faster and requires $15$ times less memory than mesh based solvers, and is not constrained to spheroidal scatterer geometries. We explore the scattered electromagnetic field distribution from wavelength scale cylindrical pillars, obtaining low-dimensional representations of our data via the singular value decomposition. We create a differentiable model fiting the input geometries and configurations of our metasurface scatterers to the low-dimensional representation of the output field. To validate our model, we inverse design two optical elements: a wavelength multiplexed element that focuses light for $λ=633$nm and produces an annular beam at $λ=400$nm and an extended depth of focus lens.
