Neural network learning with photonics and for photonic circuit design

Affiliations

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.

Editorial

Daniel Brunner et al. Nanophotonics. 2023.

. 2023 Mar 2;12(5):773-775.

doi: 10.1515/nanoph-2023-0123. eCollection 2023 Mar.

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.

No abstract available

Keywords: artificial neural networks; nanophotonics; neural network assisted photonic design; photonic neural networks.

References

1. Shen Y., Harris N. C., Skirlo S., et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics. 2017;11(7):441–446. doi: 10.1038/nphoton.2017.93. - DOI
1. Feldmann J., Youngblood N., Wright C. D., Bhaskaran H., Pernice W. H. P. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature. 2019;569(7755):208–214. doi: 10.1038/s41586-019-1157-8. - DOI - PMC - PubMed
1. Liu D., Tan Y., Khoram E., Yu Z. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics. 2018;5(4):1365–1369. doi: 10.1021/acsphotonics.7b01377. - DOI
1. Chen Y., Lu L., Karniadakis G. E., Dal Negro L. Physics-informed neural networks for inverse problems in nano-optics and metamaterials. Opt. Express. 2020;28(8):11618–11633. doi: 10.1364/oe.384875. - DOI - PubMed
1. Shastri B. J., Tait A. N., Ferreira de Lima T., et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics. 2021;15(2):102–114. doi: 10.1038/s41566-020-00754-y. - DOI