Frequency-extended inverse design of transmission-type linear-to-circular polarization control metasurface based on deep learning

Abstract Due to the parameter range limitations of the training dataset, traditional inverse prediction network models can only predict structure parameters of the metasurface within a limited frequency range. When the given design targets exceed the prediction range of network models, the predicted results will not match the actual results. This paper proposes a frequency-extended inverse design method (FEIDM) based on deep learning to address the problem. The method can automatically collect the required data and train the network model based on the center working frequency of the design targets, thereby achieving accurate prediction of metasurface structural parameters and effectively reducing labor and computational costs. Taking the transmission-type linear-to-circular polarization control metasurface as an example, the unit cell of the metasurface is first established in the paper. The structural parameters and corresponding electromagnetic parameters were collected without changing the unit size of the metasurface, and an initial inverse prediction network model (IIPNM) was constructed. The research results indicate that its predictable center working frequency range is 3-5.5 GHz. Using the design concept proposed in this paper, a program was constructed, it can automatically achieve data collection, target extraction, network model training, and prediction. Four given design targets were predicted. Among them, the center working frequencies of the three design targets are outside the initial predictable range. The predicted results meet the requirements of the given target, verifying the effectiveness of the proposed scheme. Finally, a set of parameters was selected to fabricate, and the experimental results are consistent with the simulation results. The research results can provide a reference for the efficient prediction of metasurface structural parameters over a wide frequency band.