Deep learning prediction of electromagnetic software calculation results has been a widely discussed issue in recent years. But the prediction accuracy was still one of the challenges to be solved. In this work, we proposed that the ResNets-10 model was used for predicting plasmonic metasurface S11 parameters. The two-stage training was performed by the k-fold cross-validation and small learning rate. After the training was completed, the prediction loss for aluminum, gold, and silver metal-insulator-metal metasurfaces was -48.45, -46.47, and -35.54, respectively. Due to the ultralow error value, the proposed network can replace the traditional electromagnetic computing method for calculation within a certain structural range. Besides, this network can finish the training process less than 1,100 epochs. This means that the network training process can effectively lower the design process time. The ResNets-10 model we proposed can also be used to design meta-diffractive devices and biosensors, thereby reducing the time required for the calculation process. The ultralow error of the network indicates that this work contributes to the development of future artificial intelligence electromagnetic computing software.

深度学习预测电磁软件计算结果是近年来广泛讨论的问题之一，本研究中，我们提出使用ResNets-10模型来预测等离子体元表面的S11参数，并通过k折交叉验证和小学习率进行两阶段训练，结果显示对于铝、金和银金属-绝缘体-金属等离子体元表面的预测损失分别为-48.45，-46.47和-35.54，由于超低的误差值，该网络可在一定结构范围内替代传统的电磁计算方法进行计算，此外，该网络的训练过程少于1,100个epochs，有效降低了设计过程时间，该提出的ResNets-10模型还可用于设计元互衍射器件和生物传感器，从而减少计算过程所需的时间，网络的超低误差表明该研究对未来人工智能电磁计算软件的发展有所贡献。

基于深度学习的金属-绝缘体-金属超表面的高准确性预测