A knowledge-inherited learning for intelligent metasurface design and assembly

The interaction of machine learning and optics/photonics is transforming the way we design new photonic structures, unearth latent physical laws, and develop intelligent photonic devices. Despite certain achievements, a major impediment persistently exists; datasets and networks are only disposable. Thus, for each new state or task, all datasets and networks have to be discarded, and it is imperative to reconstruct new datasets and networks, leading to an enormous waste of resources. In machine learning-based metamaterial designs, much effort has been inaugurated to enlarge the training dataset or construct specific networks. Either way, each metamaterial is physically separated, and the data utilization efficiency is very low.