Reverse design of high-degree-of-freedom metasurface based on generative adversarial network model
To solve the problem that the traditional method of designing metasurfaces with different electromagnetic responses requires a lot of time and computing resources.It is necessary to break the bottleneck of the application of metasurfaces in the field of wavefront regulation.This paper introduces a model that combines generative adversarial neural networks and predictive neural networks into the design process to achieve rapid and accurate reverse design of high-degree-of-freedom meta-atoms.The research demonstrates that using the convolutional network model as a spectral predictor,instead of complex electromagnetic numerical simulations,can achieve fast and accurate analysis of the electromagnetic response.The generative adversarial network replaces the traditional iterative optimization trial-and-error method,and achieves the goal of quickly generating multiple candidate meta-atomic structures meeting design requirements.This model combines process tolerance analysis and other methods to provide reliable design methods for novel applications such as holographic displays and optical functional devices.
万方数据
维普
