In this paper, we propose a deep learning approach for forward modeling and inverse design of photonic devices containing embedded active metasurface structures. In particular, we demonstrate that combining neural network design of metasurfaces with scattering matrix-based optimization significantly simplifies the computational overhead while facilitating accurate objective-driven design. As an example, we apply our approach to the design of a continuously tunable bandpass filter in the mid-wave infrared, featuring narrow passband (∼10 nm), high quality factors (Q-factors ∼ 102), and large out-of-band rejection (optical density ≥ 3). The design consists of an optical phase-change material Ge2Sb2Se4Te (GSST) metasurface atop a silicon heater sandwiched between two distributed Bragg reflectors (DBRs). The proposed design approach can be generalized to the modeling and inverse design of arbitrary response photonic devices incorporating active metasurfaces.

中文翻译：

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深度神经网络支持主动超表面嵌入式设计

在本文中，我们提出了一种深度学习方法，用于对包含嵌入式有源超表面结构的光子器件进行正向建模和逆向设计。特别是，我们证明了将超表面的神经网络设计与基于散射矩阵的优化相结合，显着简化了计算开销，同时促进了准确的目标驱动设计。例如，我们将我们的方法应用于中波红外中连续可调带通滤波器的设计，具有窄通带（~10 nm）、高质量因子（问-因子 ∼ 102)，以及大的带外抑制（光密度≥3）。该设计由一种光学相变材料 Ge2锑2硒4Te (GSST) 超表面位于夹在两个分布式布拉格反射器 (DBR) 之间的硅加热器上。所提出的设计方法可以推广到包含有源超表面的任意响应光子器件的建模和逆向设计。