Design of a Terahertz Metasurface Absorber Based on Machine Learning Technique

Document Type : پژوهشی


Faculty of Engineering, University of Garmsar, Garmsar, Iran


The development of metasurface absorbers offers a potential solution to achieve low weight, thin thickness, favorable absorption rate, and acceptable terahertz absorption characteristics. In order to optimize the absorption properties of metasurfaces, the absorption spectrum is usually used as an important evaluation criterion, which can show many important characteristics such as the rate of absorption at different frequencies. However, the analysis of absorption spectra related to a large number of variable structural parameters is required when designing the structure, which consumes a lot of resources and time, because electromagnetic wave absorption involves the processes of complex impedance matching and electric field excitation. To address this issue, this study proposes a machine learning approach based on a random forest algorithm to predict absorption rates based on structural parameters, reducing the need for numerical simulation and spectrum analysis time. With the random forest model, the absorption rate is predicted with the R2 score of more than 0.99. In addition, the proposed absorber design has the advantages of being thin, insensitive to polarization and with a relatively stable incident angle, due to the symmetry of the structure. This study presents a practical and effective approach for the design of complex systems related to absorbing, reflecting and transmitting electromagnetic wave propagation.


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