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Neural Inverse Design of Nanostructures (NIDN)
In the recent decade, computational tools have become central in material design, allowing rapid development cycles at reduced costs. Machine learning tools are especially on the rise in photonics. However, the inversion of the Maxwell equations needed for the design is particularly challenging from...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9780235/ https://www.ncbi.nlm.nih.gov/pubmed/36550167 http://dx.doi.org/10.1038/s41598-022-26312-w |
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author | Gómez, Pablo Toftevaag, Håvard Hem Bogen-Storø, Torbjørn Aranguren van Egmond, Derek Llorens, José M. |
author_facet | Gómez, Pablo Toftevaag, Håvard Hem Bogen-Storø, Torbjørn Aranguren van Egmond, Derek Llorens, José M. |
author_sort | Gómez, Pablo |
collection | PubMed |
description | In the recent decade, computational tools have become central in material design, allowing rapid development cycles at reduced costs. Machine learning tools are especially on the rise in photonics. However, the inversion of the Maxwell equations needed for the design is particularly challenging from an optimization standpoint, requiring sophisticated software. We present an innovative, open-source software tool called Neural Inverse Design of Nanostructures (NIDN) that allows designing complex, stacked material nanostructures using a physics-based deep learning approach. Instead of a derivative-free or data-driven optimization or learning method, we perform a gradient-based neural network training where we directly optimize the material and its structure based on its spectral characteristics. NIDN supports two different solvers, rigorous coupled-wave analysis and a finite-difference time-domain method. The utility and validity of NIDN are demonstrated on several synthetic examples as well as the design of a 1550 nm filter and anti-reflection coating. Results match experimental baselines, other simulation tools, and the desired spectral characteristics. Given its full modularity in regard to network architectures and Maxwell solvers as well as open-source, permissive availability, NIDN will be able to support computational material design processes in a broad range of applications. |
format | Online Article Text |
id | pubmed-9780235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97802352022-12-24 Neural Inverse Design of Nanostructures (NIDN) Gómez, Pablo Toftevaag, Håvard Hem Bogen-Storø, Torbjørn Aranguren van Egmond, Derek Llorens, José M. Sci Rep Article In the recent decade, computational tools have become central in material design, allowing rapid development cycles at reduced costs. Machine learning tools are especially on the rise in photonics. However, the inversion of the Maxwell equations needed for the design is particularly challenging from an optimization standpoint, requiring sophisticated software. We present an innovative, open-source software tool called Neural Inverse Design of Nanostructures (NIDN) that allows designing complex, stacked material nanostructures using a physics-based deep learning approach. Instead of a derivative-free or data-driven optimization or learning method, we perform a gradient-based neural network training where we directly optimize the material and its structure based on its spectral characteristics. NIDN supports two different solvers, rigorous coupled-wave analysis and a finite-difference time-domain method. The utility and validity of NIDN are demonstrated on several synthetic examples as well as the design of a 1550 nm filter and anti-reflection coating. Results match experimental baselines, other simulation tools, and the desired spectral characteristics. Given its full modularity in regard to network architectures and Maxwell solvers as well as open-source, permissive availability, NIDN will be able to support computational material design processes in a broad range of applications. Nature Publishing Group UK 2022-12-22 /pmc/articles/PMC9780235/ /pubmed/36550167 http://dx.doi.org/10.1038/s41598-022-26312-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Gómez, Pablo Toftevaag, Håvard Hem Bogen-Storø, Torbjørn Aranguren van Egmond, Derek Llorens, José M. Neural Inverse Design of Nanostructures (NIDN) |
title | Neural Inverse Design of Nanostructures (NIDN) |
title_full | Neural Inverse Design of Nanostructures (NIDN) |
title_fullStr | Neural Inverse Design of Nanostructures (NIDN) |
title_full_unstemmed | Neural Inverse Design of Nanostructures (NIDN) |
title_short | Neural Inverse Design of Nanostructures (NIDN) |
title_sort | neural inverse design of nanostructures (nidn) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9780235/ https://www.ncbi.nlm.nih.gov/pubmed/36550167 http://dx.doi.org/10.1038/s41598-022-26312-w |
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