Machine learning for metasurfaces broadband absorber design

Metasurfaces have been emerging increasingly due to their realization of various technologies in meeting the design of multi-functional, compact, highly efficient, tunable, and low-cost designs owing to the fact that they can manipulate electromagnetic (EM) waves in a sub-wavelength thickness. In the optical regime, they have been successful in realizing transmission, reflection and absorption for a wide range of interesting applications. The metasurface absorbers have found place in energy harvesting applications. However, their design and analysis is carried out using EM solvers which in general are heavily time-consuming due to their iterative nature of solving a problem. To mitigate the problem of slackness and computational burdensome, the machine learning (ML) is becoming popular for tackling the data related problems and have been in use for making the design of metasurfaces faster. In this work, three ML algorithms namely, XGBoost, Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) have been applied both in forward and inverse topologies for a tungsten based square-ring meta-absorber. The inverse training has been carried out by employing “principal component analysis” (PCA). The operation of a meta-absorber is dependent on its geometry; thus, the training has been carried out by varying all the geometrical features of the unit element under study. The prediction performance of the presented regression models is reckoned to be accurate that the predicted values are in the near vicinity of ground truth values. The minimum MSE for the forward model attained for the case of RF is 5.08 ×10⁻³ and that of R² is 0.9952, whereas for the inverse model, the minimum MSE of 2.05 and R² score of 0.958 with 200 PCA components is achieved. The prediction time is minimum for the LASSO algorithm which is as low as one second. The lower computation time, reliable prediction, and model-free nature of ML techniques have made them useful against data imperfections and are proven to be an effective solution to time-consuming and computationally expensive tools for metasurface design.