Deep learning for the design of photonic structures
| Autor: | Zhaxylyk A. Kudyshev, Alexandra Boltasseva, Yongmin Liu, Zhaocheng Liu, Wenshan Cai, Wei Ma |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Deep learning 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Atomic and Molecular Physics and Optics Field (computer science) Electronic Optical and Magnetic Materials 010309 optics Computer architecture 0103 physical sciences Key (cryptography) Artificial intelligence Photonics 0210 nano-technology business Abstraction (linguistics) |
| Zdroj: | Nature Photonics. 15:77-90 |
| ISSN: | 1749-4893 1749-4885 |
| DOI: | 10.1038/s41566-020-0685-y |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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