Intelligent design of broadband metasurface based on spectrum prediction neural network and transition simulated annealing algorithm

Recently, metasurfaces have demonstrated considerable potential for application across various fields, including wireless communication, Internet of Things, holographic imaging, and radar systems. With the rapid development of artificial intelligence and its application in other disciplines, metasurface design based on artificial intelligence techniques, particularly deep learning, has attracted widespread attention. In this paper, we propose a novel scheme that integrates deep neural networks with an enhanced simulated annealing algorithm to achieve intelligent design of broadband metasurfaces. Firstly, we construct and train a neural network capable of accurately predicting the amplitude and phase responses of an isotropic metasurface structure, achieving a Mean Absolute Error of 1.36° on the test set. Subsequently, we develop a novel simulated annealing algorithm that is integrated with the prediction neural network to form an intelligent optimization framework. This approach can effectively search for the solution space to accurately achieve arbitrary target phase response, with a very low Mean Squared Error calculated on the test set. Furthermore, we validate the effectiveness of the proposed model and algorithm through a practical application that demonstrates broadband dispersion characteristics within the frequency range of 9–11 Gigahertz (GHz). Additionally, the prediction model can employ transfer learning strategy to adapt to various operating frequency bands, significantly enhancing its generalization capabilities. The proposed intelligent design methodology introduces a novel pathway for rapidly designing metasurface in complex application scenarios, serving as a valuable reference for the engineering design of devices in electronics, optics, and wireless communication. • Prediction neural network with high prediction accuracy for phase, maintaining flexibility and computational efficiency. • A novel simulated annealing algorithm for multi-dimensional phase optimization with the risk of converging to local optima. • Working band can be extended by transfer learning, enhancing the generalization ability and application potential.