Fussy Inverse Design of Metamaterial Absorbers Assisted by a Generative Adversarial Network

The increasing demands for metasurfaces have led researchers to seek effective inverse design methods, which are counting on the developments in the optimization theory and deep learning techniques. Early approaches of the inverse design based on deep learning established a unique mapping between the device's geometry parameters and its designated EM characteristics. However, the generated solution based on the traditional inverse design method may not be applicable due to practical fabrication conditions. The designers sometimes want to choose the most practical one from multiple schemes which can all meet the requirements of the given EM indicators. A fuzzy inverse design method is quite in demand. In this study, we proposed a fuzzy inverse design method for metamaterial absorbers based on the generative adversarial network (GAN). As a data-driven method, self-built data sets are constructed and trained by the GAN, which contain the absorber's design parameters and their corresponding spectral response. After the training process is finished, it can generate multiple possible schemes which can satisfy the customized absorptivity and frequency bands for absorbers. The parameters generated by this model include structure sizes and impedance values, which indicates that it has the ability to learn a variety of features. The effectiveness and robustness of the proposed method have been verified by several examples for the design of both narrowband and broadband metamaterial absorbers. Our work proves the feasibility of using deep learning methods to break the limits of one-to-one mapping for the traditional inverse design method. This method may have profound usage for more complex EM device design problems in the future.