# A deep learning approach for inverse design of the metasurface for dual-polarized waves

arXiv: https://arxiv.org/abs/2105.08508 · HTML: https://mlsanity.com/paper/2105.08508/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 8 of 8)

- [Deep neural networks for the evaluation and design of photonic devices](https://mlsanity.com/paper/2007.00084/) (2020)
- [Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead](https://mlsanity.com/paper/2004.09352/) (2020)
- [Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures](https://mlsanity.com/paper/1902.03865/) (2019)
- [Reconfigurable Intelligent Surfaces vs. Relaying: Differences, Similarities, and Performance Comparison](https://mlsanity.com/paper/1908.08747/) (2019)
- [Deep neural network-based automatic metasurface design with a wide frequency range](https://mlsanity.com/paper/2101.10866/) (2021)
- [Machine-learning techniques for the optimal design of acoustic metamaterials](https://mlsanity.com/paper/1908.10645/) (2019)
- [Some useful approximations for calculation of directivities of multibeam power patterns of large planar arrays](https://mlsanity.com/paper/2006.10423/) (2020)
- [EEGsig: an open-source machine learning-based toolbox for EEG signal processing](https://mlsanity.com/paper/2010.12877/) (2020)

## Built on by (most cited first, 4 of 4)

- [Simultaneous estimation of wall and object parameters in TWR using deep neural network](https://mlsanity.com/paper/2111.04568/) (2021)
- [Deep Learning Framework for the Design of Orbital Angular Momentum Generators Enabled by Leaky-wave Holograms](https://mlsanity.com/paper/2304.12695/) (2023)
- [Deep-learning-assisted reconfigurable metasurface antenna for real-time holographic beam steering](https://mlsanity.com/paper/2406.14585/) (2024)
- [AI-Powered Inverse Design of Ku-Band SIW Resonant Structures by Iterative Residual Correction Network](https://mlsanity.com/paper/2505.06936/) (2025)

## Abstract (arXiv)

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.