Appl Opt. 2023 May 1;62(13):3502-3511. doi: 10.1364/AO.487867.
This paper proposes a deep-learning-assisted design method for 2-bit coding metasurfaces. This method uses a skip connection module and the idea of an attention mechanism in squeeze-and-excitation networks based on a fully connected network and a convolutional neural network. The accuracy limit of the basic model is further improved. The convergence ability of the model increased nearly 10 times, and the mean-square error loss function converges to 0.000168. The forward prediction accuracy of the deep-learning-assisted model is 98%, and the accuracy of inverse design results is 97%. This approach offers the advantages of an automatic design process, high efficiency, and low computational cost. It can serve users who lack metasurface design experience.
本文提出了一种基于全连接网络和卷积神经网络的深度学习辅助的 2 位编码超表面设计方法。该方法使用了跳连接模块和注意力机制的思想。进一步提高了基本模型的精度极限。模型的收敛能力提高了近 10 倍，均方误差损失函数收敛到 0.000168。深度学习辅助模型的正向预测精度为 98%，逆向设计结果的精度为 97%。该方法具有设计过程自动化、效率高、计算成本低等优点。它可以为缺乏超表面设计经验的用户提供服务。
