Neural network learning with photonics and for photonic circuit design-Reference-Cited by-同舟云学术

Neural network learning with photonics and for photonic circuit design

Published:2023-03-01 Issue:5 Volume:12 Page:773-775
ISSN:2192-8614
Container-title:Nanophotonics
language:en
Short-container-title:

Author:

Brunner Daniel¹^ORCID,Soriano Miguel C.²^ORCID,Fan Shanhui³^ORCID

Affiliation:

1. FEMTO-ST/Optics Dept., UMR CNRS 6174 , Univ. Franche-Comté , 15B avenue des Montboucons, 25030 Besançon Cedex , France

2. Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC) , Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca , Spain

3. Stanford University , Stanford , CA , USA

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials,Biotechnology

Link

https://www.degruyter.com/document/doi/10.1515/nanoph-2023-0123/pdf

Reference28 articles.

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2. J. Feldmann, N. Youngblood, C. D. Wright, H. Bhaskaran, and W. H. P. Pernice, “All-optical spiking neurosynaptic networks with self-learning capabilities,” Nature, vol. 569, no. 7755, pp. 208–214, 2019. https://doi.org/10.1038/s41586-019-1157-8.

3. D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics, vol. 5, no. 4, pp. 1365–1369, 2018. https://doi.org/10.1021/acsphotonics.7b01377.

4. Y. Chen, L. Lu, G. E. Karniadakis, and L. Dal Negro, “Physics-informed neural networks for inverse problems in nano-optics and metamaterials,” Opt. Express, vol. 28, no. 8, pp. 11618–11633, 2020. https://doi.org/10.1364/oe.384875.

5. B. J. Shastri, A. N. Tait, T. Ferreira de Lima, et al.., “Photonics for artificial intelligence and neuromorphic computing,” Nat. Photonics, vol. 15, no. 2, pp. 102–114, 2021. https://doi.org/10.1038/s41566-020-00754-y.

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