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...
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| Veröffentlicht in: | Nature photonics 2021-02, Vol.15 (2), p.77-90 |
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| container_title | Nature photonics |
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| creator | Ma, Wei Liu, Zhaocheng Kudyshev, Zhaxylyk A. Boltasseva, Alexandra Cai, Wenshan Liu, Yongmin |
| description | 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.
The application of deep learning to the design of photonic structures and devices is reviewed, including algorithm fundamentals. |
| doi_str_mv | 10.1038/s41566-020-0685-y |
| format | Article |
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| subjects | 639/624/399/1015 639/624/399/1022 639/624/399/1099 639/766/1130/2799 639/925/927/1021 Algorithms Applied and Technical Physics Deep learning Design Design optimization Learning algorithms Machine learning Optics Photonics Physical Sciences Physics Physics and Astronomy Physics, Applied Quantum Physics Review Article Science & Technology Spawning |
| title | Deep learning for the design of photonic structures |
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