Artificial neural networks for inverse design of resonant nanophotonic components with oscillatory loss landscapes
Machine learning offers the potential to revolutionize the inverse design of complex nanophotonic components. Here, we propose a novel variant of this formalism specifically suited for the design of resonant nanophotonic components. Typically, the first step of an inverse design process based on mac...
Main Authors: | Lenaerts Joeri, Pinson Hannah, Ginis Vincent |
---|---|
Format: | Article |
Language: | English |
Published: |
De Gruyter
2020-11-01
|
Series: | Nanophotonics |
Subjects: | |
Online Access: | https://doi.org/10.1515/nanoph-2020-0379 |
Similar Items
-
End-to-end nanophotonic inverse design for imaging and polarimetry
by: Lin Zin, et al.
Published: (2020-12-01) -
Deep learning enabled inverse design in nanophotonics
by: So Sunae, et al.
Published: (2020-02-01) -
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale
by: Yao Kan, et al.
Published: (2019-01-01) -
A directional Gaussian smoothing optimization method for computational inverse design in nanophotonics
by: Jiaxin Zhang, et al.
Published: (2021-01-01) -
Nanophotonic particle simulation and inverse design using artificial neural networks
by: Peurifoy, John, et al.
Published: (2022)