Abstract Complex nanophotonic structures hold the potential to deliver exquisitely tailored optical responses for a range of applications. Metal–insulator–metal (MIM) metasurfaces arranged in supercells, for instance, can be tailored by geometry and material choice to exhibit a variety of absorption properties and resonant wavelengths. With this flexibility, however, comes a vast space of design possibilities that classical design paradigms struggle to effectively navigate. To overcome this challenge, here, we demonstrate a tandem residual network approach to efficiently generate multiplexed supercells through inverse design. By using a training dataset with several thousand full-wave electromagnetic simulations in a design space of over three trillion possible designs, the deep learning model can accurately generate a wide range of complex supercell designs given a spectral target. Beyond inverse design, the presented approach can also be used to explore the structure–property relationships of broadband absorption and emission in such supercell configurations. Thus, this study demonstrates the feasibility of high-dimensional supercell inverse design with deep neural networks, which is applicable to complex nanophotonic structures composed of multiple subunit elements that exhibit coupling.

中文翻译：

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具有串联残差网络的多重超单元超表面设计和优化

摘要 复杂的纳米光子结构具有为一系列应用提供精心定制的光学响应的​​潜力。例如，排列在超级电池中的金属-绝缘体-金属（MIM）超表面可以通过几何形状和材料选择进行定制，以表现出各种吸收特性和共振波长。然而，这种灵活性带来了广阔的设计可能性空间，经典设计范式难以有效导航。为了克服这一挑战，在这里，我们展示了一种串联残差网络方法，可通过逆向设计有效地生成多路复用超级单元。通过在超过 3 万亿个可能设计的设计空间中使用包含数千个全波电磁仿真的训练数据集，给定光谱目标，深度学习模型可以准确地生成各种复杂的超胞设计。除了逆向设计之外，所提出的方法还可以用于探索这种超级电池配置中宽带吸收和发射的结构-特性关系。因此，本研究证明了具有深度神经网络的高维超胞逆向设计的可行性，该设计适用于由表现出耦合的多个子单元元素组成的复杂纳米光子结构。