深度学习赋能微纳光子学材料设计研究进展

光子学结构设计是微纳光学器件和系统研究的核心。许多人工设计的光子学结构，比如超材料、光子晶体、等离激元纳米结构等，已经在高速可视通信、高灵敏度传感和高效能源收集及转换中得到了广泛的应用。然而，该领域中通用的设计方法是基于简化的物理解析模型及基于规则的数值模拟方法，属于反复试错的方法，效率低且很可能会错过最佳的设计参数。因此，快速得到设计参数和光谱响应信息之间的潜在关联性，是实现光子学器件高效设计的关键。在过去的几年里，深度学习在语言识别、机器视觉、自然语言处理等领域发展迅速。深度学习的独特优势在于其数据驱动的方法，可以让模型从海量数据中自动发现有用的信息，这为解决上述光子学结构设计问题提供了一种全新的方法。本篇综述从不同的微纳光子学结构设计的应用场景出发，介绍了不同的深度学习模型在光子学设计领域中的适用范围和选择依据，并对该领域未来的机遇与挑战进行了总结与展望。

Metamaterials and negative refractive index

The structure design is the core of micro-nanophotonic devices and optical systems. Many artificially designed photonic structures, such as metamaterials, photonic crystals, and plasmonic nanostructures, have been widely used in high-speed visible communication, high-sensitivity sensing, and efficient energy harvesting and conversion. However, standard design methods in this field are based on simplified physical analytical model and rule-based numerical simulation method, which is a trial-and-error method, low efficiency and likely to miss the optimal design parameters. Therefore, rapidly acquiring the potential correlation between design parameters and spectral response information is the key for realizing the efficient design of photonic devices. Besides, deep learning (DL) has been developed rapidly in fields such as language recognition, machine vision, and natural language processing in the past few years. The unique advantage of DL lies in its data-driven algorithm, which allows models for discovering useful information from massive amounts of data automatically and provides a new route to solve the aforementioned design problems of photonic structures. This review starts from different application scenarios of micro-nano photonics structure design, introduces the application scope and selection basis of various DL models in the field of photonics design, and summarizes and looks forward to future opportunities and challenges in this field.