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**Chapter 4**

**Abstract**

**1. Introduction**

Deep Learning Enabled

*Lujun Huang, Lei Xu and Andrey E. Miroshnichenko*

Deep learning has become a vital approach to solving a big-data-driven problem.

It has found tremendous applications in computer vision and natural language processing. More recently, deep learning has been widely used in optimising the performance of nanophotonic devices, where the conventional computational approach may require much computation time and significant computation source. In this chapter, we briefly review the recent progress of deep learning in nanophotonics. We overview the applications of the deep learning approach to optimising the various nanophotonic devices. It includes multilayer structures, plasmonic/ dielectric metasurfaces and plasmonic chiral metamaterials. Also, nanophotonic can directly serve as an ideal platform to mimic optical neural networks based on nonlinear optical media, which in turn help to achieve high-performance photonic

chips that may not be realised based on conventional design method.

metasurface, chiral metamaterials, all-optical neural network

**Keywords:** deep learning, inverse design, plasmonic metasurface, dielectric

In the past several decades, nanophotonics has been demonstrated as an ideal platform to manipulate the light-matter interaction and engineer the wavefront of the electromagnetic wave at will. The rapid development on nanophotonics has led to tremendous applications ranged from lasing, Lidar, biosensor, LED, photodetector, integrated photonic circuit, invisibility cloak, etc. Nanophotonics covers many exciting topics: photonic crystal, plasmonics, metamaterials, and nanophotonics based on some novel materials (e.g., two-dimensional materials, perovskite). Currently, the building blocks for nanophotonics are made from either metallic or dielectric elements with regular shapes, such as rectangular wire, cylinder, cuboids, and sphere for plasmonic and dielectric metasurfaces. Usually, limited parameters are provided for such a regular structure, and, thus, the optimisation process can be done in a reasonable short time. For example, a single dielectric cylinder with only two parameters, including diameter and height, are involved. Due to the limited freedom, the performance of photonic devices based on the regular pattern is far away from the optimal one. Inverse design method has been widely used to tackle this problem because the full parameter space can be explored [1]. Conventional inverse design methods that include topology optimisation, genetic algorithm, steep descent, and particle swarming optimisation shown in **Figure 1a**, however, require the vast computational source and take a long time to find the optimal

Nanophotonics
