Innovative approaches and tools play an important role in shaping design, characterization and optimization for the field of photonics. As a subset of machine learning that learns multilevel abstraction of data using hierarchically structured layers, deep learning offers an efficient means to design photonic structures, spawning data-driven approaches complementary to conventional physics- and rule-based methods. Here, we review recent progress in deep-learning-based photonic design by providing the historical background, algorithm fundamentals and key applications, with the emphasis on various model architectures for specific photonic tasks. We also comment on the challenges and perspectives of this emerging research direction.

创新的方法和工具在塑造光子学领域的设计，表征和优化中起着重要作用。作为机器学习的子集，它使用分层结构的层来学习数据的多层抽象，深度学习提供了一种有效的手段来设计光子结构，产生了与传统的基于物理和基于规则的方法互补的数据驱动方法。在这里，我们通过提供历史背景，算法基础知识和关键应用，重点介绍用于特定光子任务的各种模型架构，来回顾基于深度学习的光子设计的最新进展。我们还评论了这一新兴研究方向的挑战和观点。