The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Generative Model for the Inverse Design of Metasurfaces
Open accessAbstract
The advent of metasurfaces in recent years has ushered in a revolutionary means to manipulate the behavior of light on the nanoscale. The design of such structures, to date, has relied on the expertise of an optical scientist to guide a progression of electromagnetic simulations that iteratively solve Maxwell's equations until a locally optimized solution can be attained. In this work, we identify a solution to circumvent this conventional design procedure by means of a deep learning architecture. When fed an input set of customer-defined optical spectra, the constructed generative network generates candidate patterns that match the on-demand spectra with high fidelity. This approach reveals an opportunity to expedite the discovery and design of metasurfaces for tailored optical responses in a systematic, inverse-design manner.
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Cites
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800
Flat optics with designer metasurfaces
Yu, N., & Capasso, F. (2014) · Nature Materials · doi:10.1038/nmat3839 · Cited by 5,984
Deep learning-based classification of hyperspectral data
Chen, Y., Lin, Z., Zhao, X., Wang, G., & Gu, Y. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2329330 · Cited by 2,679
Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847
Machine learning phases of matter
Carrasquilla, J., & Melko, R. G. (2017) · Nature Physics · doi:10.1038/nphys4035 · Cited by 1,448
Performing Mathematical Operations with Metamaterials
Silva, A., Monticone, F., Castaldi, G., Galdi, V., Alù, A., & Engheta, N. (2014) · Science · doi:10.1126/science.1242818 · Cited by 1,136
Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer
Piggott, A. Y., Lu, J., Lagoudakis, K. G., Petykiewicz, J., Babinec, T. M., & Vučković, J. (2015) · Nature Photonics · doi:10.1038/nphoton.2015.69 · Cited by 1,113
Topology-Optimized Multilayered Metaoptics
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Cited by
Physics-Informed Neural Networks with Hard Constraints for Inverse Design
Lu, L., Pestourie, R., Yao, W., Wang, Z., Verdugo, F., & Johnson, S. G. (2021) · SIAM Journal on Scientific Computing · doi:10.1137/21m1397908 · Cited by 771
Ma, W., Cheng, F., Xu, Y., Wen, Q., & Liu, Y. (2019) · Advanced Materials · doi:10.1002/adma.201901111 · Cited by 615
Deep learning enabled inverse design in nanophotonics
So, S., Badloe, T., Noh, J., Rho, J., & Bravo‐Abad, J. (2020) · Nanophotonics · doi:10.1515/nanoph-2019-0474 · Cited by 564
Inverse-designed spinodoid metamaterials
Kumar, S., Tan, S., Zheng, L., & Kochmann, D. M. (2020) · npj Computational Materials · doi:10.1038/s41524-020-0341-6 · Cited by 444
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
Metadata
- Authors
- Liu, Z., Zhu, D., Rodrigues, S. P., Lee, K.-T., & Cai, W.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-09-12
- Volume
- 18
- Issue
- 10
- Pages
- 6570-6576
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W2803281408
- Cited by
- 971
- References
- 9
- FWCI
- 22.61
- Type
- journal article
- Language
- en
- Open access
- green
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- Liu, Z., Zhu, D., Rodrigues, S. P., Lee, K.-T., & Cai, W. (2018). Generative Model for the Inverse Design of Metasurfaces. Nano Letters. https://doi.org/10.1021/acs.nanolett.8b03171
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