A deep learning approach for inverse design of the metasurface for dual-polarized waves
This work provides an efficient strategy for metasurface designers, though it is incremental as it builds on existing machine learning approaches with a restricted output structure.
The paper tackles the inverse design of metasurfaces for dual-polarized waves by using a deep neural network to generate unit cell structures across an ultra-wide frequency band (4-45 GHz), achieving 91% accuracy and reducing computational time compared to conventional methods.
Compared to the conventional metasurface design, machine learning-based methods have recently created an inspiring platform for an inverse realization of the metasurfaces. Here, we have used the Deep Neural Network (DNN) for the generation of desired output unit cell structures in an ultra-wide working frequency band for both TE and TM polarized waves. To automatically generate metasurfaces in a wide range of working frequencies from 4 to 45 GHz, we deliberately design an 8 ring-shaped pattern in such a way that the unit-cells generated in the dataset can produce single or multiple notches in the desired working frequency band. Compared to the general approach, whereby the final metasurface structure may be formed by any randomly distributed "0" and "1", we propose here a restricted output structure. By restricting the output, the number of calculations will be reduced and the learning speed will be increased. Moreover, we have shown that the accuracy of the network reaches 91\%. Obtaining the final unit cell directly without any time-consuming optimization algorithms for both TE and TM polarized waves, and high average accuracy, promises an effective strategy for the metasurface design; thus, the designer is required only to focus on the design goal.