Nanophotonic particle simulation and inverse design using artificial neural networks
We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate su...
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| Veröffentlicht in: | Science advances 2018-06, Vol.4 (6), p.eaar4206-eaar4206 |
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| container_issue | 6 |
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| container_title | Science advances |
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| creator | Peurifoy, John Shen, Yichen Jing, Li Yang, Yi Cano-Renteria, Fidel DeLacy, Brendan G Joannopoulos, John D Tegmark, Max Soljačić, Marin |
| description | We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical. |
| doi_str_mv | 10.1126/sciadv.aar4206 |
| format | Article |
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| source | DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Computer Science Optics SciAdv r-articles |
| title | Nanophotonic particle simulation and inverse design using artificial neural networks |
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