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 metasurface 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 in which 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 restrict 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, an ultra-wide working frequency, and high average accuracy equip an inspiring platform for engineering projects without the need for complex electromagnetic theory.
Highlights
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 metasurface design
Due to its remarkable potential to provide less computational resources, more accuracy, less design time, and more flexibility, machine learning has been entered in various wave-interaction phenomena, such as Electromagnetic Compatibility (EMC)[24,25], Antenna Optimization and D esign[26,27], All-Dielectric M etasurface[28], Optical and photonic s tructures[29], and Plasmonic nanostructure[30]
Cui et al have proposed a deep learning-based metasurface design method named REACTIVE, which is capable of detecting the inner rules between a unit-cell building and its EM properties with an average accuracy of 76.5%31
Summary
We have proposed an inverse metasurface design method based on a deep neural network, whereby metasurface structures may be computed directly by merely specifying the design targets. After training the deep learning model by a set of samples, our proposed model can automatically generate the metasurface pattern as the output by four specified reflection criteria (namely, number of resonances, resonance frequencies, resonance depths, and resonance bandwidths) as the input in an ultra-wide operating frequency. Comparing the numerical simulations with the desired design target illustrates that our proposed approach successfully generates the required metasurface structures with an accuracy of more than 90%. By using 8 ring-shaped patterns during the training process and restricting the output of the network to generate a 48 size vector, our presented method serves as a fast and effective approach in terms of computational iterations, design time consumption, and network accuracy. The presented DNN-based method can pave the way for new research avenues in automatic metasurface realization and highly complicated wave manipulations
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