Feature-based machine learning for the efficient design of nanophotonic structures-Reference-Cited by-同舟云学术

Feature-based machine learning for the efficient design of nanophotonic structures

Published:2022-12 Issue: Volume:52 Page:101077
ISSN:1569-4410
Container-title:Photonics and Nanostructures - Fundamentals and Applications
language:en
Short-container-title:Photonics and Nanostructures - Fundamentals and Applications

Author:

Ferranti Francesco

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Condensed Matter Physics,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference28 articles.

1. Deep-learning-enabled on-demand design of chiral metamaterials;Ma;ACS Nano,2018

2. Nanophotonic particle simulation and inverse design using artificial neural networks;Peurifoy;Sci. Adv.,2018

3. Training deep neural networks for the inverse design of nanophotonic structures;Liu;ACS Photon.,2018

4. A deep neural network for generalized prediction of the near fields and far fields of arbitrary 3D nanostructures;Muskens,2020

5. Deep learning in nano-photonics: inverse design and beyond;Wiecha;Photon. Res.,2021

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1. Inverse design of high efficiency and large bandwidth power splitter for arbitrary power ratio based on deep residual network;Optical and Quantum Electronics;2024-01-30

2. Hybrid inverse design scheme for nanophotonic devices based on encoder-aided unsupervised and supervised learning;Optics Express;2023-11-08

3. Gaussian Process Regression for the Modeling of Metalenses;2023 Photonics & Electromagnetics Research Symposium (PIERS);2023-07-03

4. Feature-Based Inverse Modeling of Nanophotonic Devices;2023 Photonics & Electromagnetics Research Symposium (PIERS);2023-07-03

5. Inverse design of nanophotonics devices and materials;Photonics and Nanostructures - Fundamentals and Applications;2022-12