M-IFNN: A Multiple Inverse–Forward Requirement-Based Deep Learning Framework for Generated Design of Metasurfaces

This article presents a fully automated inverse-forward design of multiple electromagnetic (EM) structure designs by using a requirement-based multiple inverse-forward neural network (M-IFNN). Compared to the traditional design method, the proposed method can obtain the structure model, geometric parameters, and S-parameters by just inputting sentences into a vector-extracted language model, which output to M-IFNN. In addition, we use the parallel-combined method to achieve inverse-forward output by only one neural network for the first time. To achieve the above target, first, we use three feature extraction algorithms to obtain the feature vector of three metasurfaces. Then, we use identification vector and filling methods to achieve multi-EM designs by one neural network. Subsequently, we use the parallel-combined method to integrate the inverse geometric parameters design and forward-parameters design into one model. Deep neural network (DNN) is chosen for M-IFNN because it has smaller storage memory and good results. The M-IFNN finally achieves a 100% correctness prediction of corresponding structures and a 0.41/0.034 mean absolute error (MAE) result for predicting inverse/forward geometric parameters/S-parameters. Three prototypes are fabricated and measured. The measurement results are in good agreement with the simulation results. The good validation results prove the feasibility of the proposed method. Additionally, several dialectical discussions about M-IFNN are at the end of the article.