Design of thin, wideband electromagnetic absorbers with polarization and angle insensitivity using deep learning.

Metamaterial-based electromagnetic absorbers, despite being thin and lightweight, typically suffer from narrow-band frequency bandwidth and sensitivity to polarization and incident angle due to their resonant nature. Previous methods to increase bandwidth have shown improvements but have not fully succeeded in developing wide-band, thin metamaterial-based absorbers suitable for mass production. In this study, we introduce a novel approach that leverages artificial intelligence to design a thin, wideband metamaterial-based absorber covering the entire frequency range of 8-12 GHz. The proposed method utilizes a Generative Adversarial Network (GAN), given the need for precise structural details and computational efficiency, which globally outperform variational autoencoders (VAEs) and diffusion models, for parameter estimation and a Multi-Layer Perceptron (MLP) network as a simulator to predict the electromagnetic response of the designed absorber and provide feedback to the generative network. Numerical full-wave electromagnetic simulations serve as the training data and ground truth for both the GAN and MLP networks. This training enables the generative network to produce structures with high absorption, while the MLP predicts the corresponding absorbance value for each structure. This approach allows for the rapid design of various real-world structures, quick calculation of their absorption values using the MLP network, and selection of the most optimal structures for fabrication. The performance of the designed metamaterial-based absorber is verified both numerically and experimentally. Results show an absorption rate above 90% for all frequencies in the range of 8-12 GHz. The structure also operates effectively for both TE and TM polarizations and for all incident angles between 0-45 degrees. Additionally, the designed structure can be easily fabricated using printed circuit board (PCB) technology, making it practical and suitable for mass production.