Abstract
We are interested in exploring the limit in using deep learning (DL) to study the electromagnetic (EM) response for complex and random metasurfaces, without any specific applications in mind. For simplicity, we focus on a simple pure reflection problem of a broadband EM plane wave incident normally on such complex metasurfaces in the frequency regime of 2–12 GHz. In doing so, we create a DL-based framework called the metasurface design deep convolutional neural network (MSDCNN) for both forward and inverse designs of three different classes of complex metasurfaces: (a) arbitrary connecting polygons, (b) basic pattern combination, and (c) fully random binary patterns. The performance of each metasurface is evaluated and cross-benchmarked. Dependent on the type of complex metasurfaces, sample size, and DL algorithms used, the MSDCNN is able to provide good agreement and can be a faster design tool for complex metasurfaces than the traditional full-wave EM simulation methods. However, no single universal deep convolutional neural network model can work well for all metasurface classes based on detailed statistical analysis (such as mean, variance, kurtosis, and mean-squared error). Our findings report important information on the advantages and limitations of current DL models in designing these ultimately complex metasurfaces.
Highlights
Metasurfaces are two-dimensional (2D) artificial structures that are designed and fabricated to manipulate the propagation of electromagnetic (EM) waves in order to achieve performance unique from that of conventional materials.1–6 They have attracted enormous research attention due to their extraordinary ability to control many electromagnetic properties, such as amplitude,7,8 phase,9–11 and polarization,12 and many types of metasurfaces have been proposed, offering a large variety of specialized metasurfaces for different applications, such as in programmable metasurfaces,13–15 transforming heat,16 cloaking,17,18 holograms,19 conversion,20 absorption,21,22 scattering reduction,23 polarization,24 transmission,25 and others.There are two general approaches in designing metasurfaces
IV, we will extend this capability to other types of complex metasurfaces in order to assess the broader performance of the metasurface design deep convolutional neural network (MSDCNN) and to understand its limitation
Having experimented on three different complex metasurfaces with a high degree of freedom—arbitrary connecting shapes (PLGs), basic pattern combination (PTN), and fully random binary shapes (RDNs)—it is confirmed that the MSDCNN can provide a promising tool for such complex metasurfaces
Summary
Metasurfaces are two-dimensional (2D) artificial structures that are designed and fabricated to manipulate the propagation of electromagnetic (EM) waves in order to achieve performance unique from that of conventional materials. They have attracted enormous research attention due to their extraordinary ability to control many electromagnetic properties, such as amplitude, phase, and polarization, and many types of metasurfaces have been proposed, offering a large variety of specialized metasurfaces for different applications, such as in programmable metasurfaces, transforming heat, cloaking, holograms, conversion, absorption, scattering reduction, polarization, transmission, and others. The first approach is the forward design, which is an iterative process involving parametric studies to explore within a given set of input parameters in order to produce the desired EM response or output. The second approach is the inverse design, which is to find an optimal set of input parameters for a given output This is more difficult than the forward design as there is no definite or unique solution for such a problem. The EM response is focused on the reflection of a broadband EM wave (from 2 to 12 GHz) on the given dataset of complex metasurfaces (PLG, PTN, and RDN), where the reflection as a function of frequency can be predicted by using different DL models based on our training procedures for both forward and inverse design. Other DL models such as the graph neural network or complex value neural network will be likely promising candidates for further improvements
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