Machine Learning-Based-Inverse Topological Design Method for Active Frequency-Selective Surface (ITDM-AFSS)

To design active frequency selective surface (AFSS) with a large number of degrees of freedom (DoF), in this communication, a machine learning based inverse topological design method (ITDM) is proposed for AFSS design, termed ITDM-AFSS. Different from the conventional FSS design, the AFSS electromagnetic responses under ON/OFF states of PIN diode simultaneously participate in the training in the proposed ITDM-AFSS, and are simultaneously considered in the design process. In contrast to parametric modeling, the topological design domain can be viewed as a combination of discrete binary pixels. This allows for a significantly larger number of DoFs, enabling the exploration of more optimal structures. The effectiveness of ITDM-AFSS is demonstrated through a numerical example, and two other popular models and an evolutionary algorithm-based design method are employed to compare with the proposed ITDM-AFSS. Additionally, measurements of the fabricated topologies are carried out to validate the performance of the AFSS design derived from ITDM-AFSS.