To expedite the design process of metasurface, an improved transfer function (TF)-based artificial neural network (ANN) model is proposed, which can directly generate structure parameters to match the customer-expected electromagnetic (EM) response. Compared with the existing inverse design techniques, poles and residues of TF instead of the EM responses at discrete points are input into the proposed model. To identify the solution to circumvent the nonuniqueness problem, which is the major challenge of inverse design, a novel network structure and a new training error function are proposed. An electromagnetically induced transparency-like metasurface is selected as an example to verify the effectiveness and efficiency of the proposed inverse design model.

中文翻译：

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用于超表面逆向设计的高效人工神经网络模型

为了加快超表面的设计过程，提出了一种改进的基于传递函数 (TF) 的人工神经网络 (ANN) 模型，该模型可以直接生成结构参数以匹配客户预期的电磁 (EM) 响应。与现有的逆向设计技术相比，将 TF 的极点和残差而不是离散点的 EM 响应输入到所提出的模型中。为了找出解决非唯一性问题的解决方案，这是逆向设计的主要挑战，提出了一种新颖的网络结构和一种新的训练误差函数。选择一个电磁感应透明的超表面作为例子来验证所提出的逆向设计模型的有效性和效率。