  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.

Machine Learning;Classical Physics