Interfacing Photonics with Artificial Intelligence
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. In the recent Nature Photonics article “Deep learning for the design of photonic structures“, MIE/ECE Associate Professor Yongmin Liu and his co-workers (including Prof. Wenshan Cai at Georgia Institute of Technology and Prof. Alexandra Boltasseva at Purdue University) 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. They also comment on the challenges and perspectives of this emerging research direction.