Photonics Research, Volume. 9, Issue 6, B247(2021)
Genetic-algorithm-based deep neural networks for highly efficient photonic device design
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Fig. 2. Encoding process that uses polar vectors and design rule constrains as a parameter vector to describe the design of a given photonic device.
Fig. 3. Schematic drawing of the DNN models of the forward and inverse design processes.
Fig. 4. Design analyses of a power splitter with splitting ratio of 2:3: (a) the evolution of the qualified population proportion; (b) and (c) the FDTD simulation result of the best devices in the initial population and the final population; (d) the distribution of optical transmission of the initial population.
Fig. 5. GDNN design examples with transmission spectrum and FDTD simulation results: (a) a 1:2 power splitter, (b) a 1:1 power splitter, (c) a TE mode converter, and (d) a broadband power splitter.
Table 1. Training Data Summary of the Designs in This Work
View tableView in ArticleTable 1. Training Data Summary of the Designs in This Work
Device Designs Initial Data Number of Iterations Number of Offspring Total Data Power splitter (1:1) 1000 35 50 2750 Power splitter (1:2) 1000 28 50 2400 Power splitter (2:3) 1000 32 50 2600 TE mode converter 1000 30 50 2500 Broadband splitter 1000 23 50 2150
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Yangming Ren, Lingxuan Zhang, Weiqiang Wang, Xinyu Wang, Yufang Lei, Yulong Xue, Xiaochen Sun, Wenfu Zhang, "Genetic-algorithm-based deep neural networks for highly efficient photonic device design," Photonics Res. 9, B247 (2021)
Paper Information
Special Issue: DEEP LEARNING IN PHOTONICS
Received: Dec. 1, 2020
Accepted: Mar. 25, 2021
Published Online: May. 27, 2021
The Author Email: Xiaochen Sun (sunxiaochen@opt.ac.cn), Wenfu Zhang (wfuzhang@opt.ac.cn)
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