Fast design and optimization method for an ultra-wideband perfect absorber based on artificial neural network acceleration

In recent years, ultra-wideband perfect absorbers (UWPAs) based on metamaterial nanostructures have been widely studied due to their excellent performance. However, designing and optimizing the absorber quickly and accurately remains challenging in the broad-band range. In this work, by adopting the genetic algorithm combined with artificial neural network (ANN) acceleration, an effective method with the global search ability and extremely fast optimization speed is developed, which only requires an accurate forward ANN model and offers tremendous controls to the designer. In the context of the optimization of the nano-multilayers absorber, the most of mean absolute errors (MAEs) of ANN between the actual spectral absorptance and the predicted spectral absorptance are less than 1E-2, demonstrating the outstanding consistency. What's more, the optimization speed is four orders of magnitude faster than conventional electromagnetic simulation software, 1.2 × 10⁵ search calculations take only 573 s. Meanwhile, we also show that the hybridization between multilayer cavity plasmonic resonant modes is the main cause of the ultra-wideband absorptance. This method significantly increases the viability of more complex ultra-wideband perfect absorber designs and provides opportunities to manipulate light-matter interactions in the future.