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.
维普
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