The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures
Open accessAbstract
Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal, methods for design and analysis of nanophotonic systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Discrete-Dipole Approximation For Scattering Calculations
Draine, B. T., & Flatau, P. J. (1994) · Journal of the Optical Society of America A · doi:10.1364/josaa.11.001491 · Cited by 3,614
Optically resonant dielectric nanostructures
Kuznetsov, A. I., Miroshnichenko, A. E., Brongersma, M. L., Kivshar, Y. S., & Luk’yanchuk, B. (2016) · Science · doi:10.1126/science.aag2472 · Cited by 2,884
Kauranen, M., & Zayats, A. V. (2012) · Nature Photonics · doi:10.1038/nphoton.2012.244 · Cited by 2,530
Large single-molecule fluorescence enhancements produced by a bowtie nanoantenna
Kinkhabwala, A., Yu, Z., Fan, S., Avlasevich, Y., Müllen, K., & Moerner, W. E. (2009) · Nature Photonics · doi:10.1038/nphoton.2009.187 · Cited by 2,047
Optical Chirality and Its Interaction with Matter
Tang, Y., & Cohen, A. E. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.163901 · Cited by 1,283
Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures
Liu, D., Tan, Y., Khoram, E., & Yu, Z. (2018) · ACS Photonics · doi:10.1021/acsphotonics.7b01377 · Cited by 1,181
Cited by
Roadmap for Optical Metasurfaces
Kuznetsov, A. I., Brongersma, M. L., Yao, J., Chen, M. K., Levy, U., Tsai, D. P., Zheludev, N. I., Faraon, A., Arbabi, A., Yu, N., et al. (2024) · ACS Photonics · doi:10.1021/acsphotonics.3c00457 · Cited by 420
Nanophotonic Structural Colors
Daqiqeh Rezaei, S., Dong, Z., You En Chan, J., Trisno, J., Ng, R. J. H., Ruan, Q., Qiu, C.-W., Mortensen, N. A., & Yang, J. K. W. (2021) · ACS Photonics · doi:10.1021/acsphotonics.0c00947 · Cited by 348
Meta-atom library generation via an efficient multi-objective shape optimization method
Whiting, E. B., Campbell, S. D., Kang, L., & Werner, D. H. (2020) · Optics Express · doi:10.1364/oe.398332 · Cited by 67
Metadata
- Authors
- Wiecha, P. R., & Muskens, O. L.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-12-11
- Volume
- 20
- Issue
- 1
- Pages
- 329-338
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W2996691366
- Cited by
- 264
- References
- 58
- FWCI
- 8.54
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures. https://app.cochat.ai/papers/10.1021/acs.nanolett.9b03971
- The paper
- Wiecha, P. R., & Muskens, O. L. (2020). Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures. Nano Letters. https://doi.org/10.1021/acs.nanolett.9b03971
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
