Deep learning for the design of photonic structures

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.

Automated summary

Innovative approaches and tools, particularly deep learning, are shaping the field of photonics by offering efficient means to design photonic structures and providing data-driven solutions complementary to traditional physics-based methods. The progress in deep-learning-based photonic design is promising, with various model architectures showing potential applications in specific photonic tasks.