**4. Conclusion and outlook**

Although deep learning was proposed and found great success in the context of computer vision and speech/image recognition, it has become a powerful approach to solve complex problems in biology, physics and chemistry. As a branch of physics, nanophotonics has witnessed huge progress based on deep learning. Deep learning allows us to inversely design nanophotonic devices with even less computation source and time compared to conventional computational approaches, such as topology optimisation and genetic algorithm. Currently, the research interests and efforts are still fast-growing and expanding in deep learning-enabled nanophotonics. More research opportunities may be brought in this area.

On the one hand, although deep learning has been successfully applied to retrieve the structure parameters for any given spectrum, it remains an opening question that whether it is possible to realise narrowband or broadband absorbers at the specified wavelength or wavelength range. On the other hand, by combining deep learning and topology optimisation, beam steering at relatively large deflection angle with high efficiency has been demonstrated for single- or bi-operation wavelengths. Next step is to utilise deep learning to optimise the metasurface design with multi-functionalities further. For example, current broadband achromatic metalens has limited focusing efficiency. We believe the deep learning can entirely overcome this limitation by providing more irregular combinations of metaatoms that cannot be found by regular cylinder metaatoms. Finally, since nanophotonics offers a powerful and versatile platform to realise optical neural networks, more advanced and fast photonic chips that can bypass the computational capability based on traditional electric chips will be developed and paved the way toward the photonic computer.
