Deep neural network-based automatic metasurface design with a wide frequency range

TL;DR

This paper presents a deep neural network approach for the inverse design of metasurfaces that operate effectively across an ultra-wide frequency range, significantly reducing design time and complexity.

Contribution

It introduces two DNN architectures for metasurface design, achieving over 90% accuracy and enabling direct structure determination without extensive optimization.

Findings

Achieved over 90% accuracy in metasurface structure generation.

Reduced computational time by restricting DNN output.

Successfully designed metasurfaces from 4 to 45 GHz.

Abstract

Beyond the scope of conventional metasurface which necessitates plenty of computational resources and time, an inverse design approach using machine learning algorithms promises an effective way for metasurfaces design. In this paper, benefiting from Deep Neural Network (DNN), an inverse design procedure of a metasurface in an ultra-wide working frequency band is presented where the output unit cell structure can be directly computed by a specified design target. To reach the highest working frequency, for training the DNN, we consider 8 ring-shaped patterns to generate resonant notches at a wide range of working frequencies from 4 to 45 GHz. We propose two network architectures. In one architecture, we restricted the output of the DNN, so the network can only generate the metasurface structure from the input of 8 ring-shaped patterns. This approach drastically reduces the computational time, while keeping the network's accuracy above 91\%. We show that our model based on DNN can satisfactorily generate the output metasurface structure with an average accuracy of over 90\% in both network architectures. Determination of the metasurface structure directly without time-consuming optimization procedures, having an ultra-wide working frequency, and high average accuracy equip an inspiring platform for engineering projects without the need for complex electromagnetic theory.