Rapid deep-learning-assisted design method for 2-bit coding metasurfaces

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.(c) 2023 Optica Publishing Group

Automated summary

This paper proposes a deep-learning-assisted design method for 2-bit coding metasurfaces. It improves the accuracy limit of the basic model and increases the convergence ability of the model by nearly 10 times. The forward prediction accuracy of the deep-learning-assisted model is 98%, and the accuracy of inverse design results is 97%. It offers the advantages of an automatic design process, high efficiency, and low computational cost.