E-mail Alert
RSS
| Citation: |
Liu YM, Li YX. Reconfigurable optical neural networks with Plug-and-Play metasurfaces. Opto-Electron Adv 7, 240057 (2024). doi: 10.29026/oea.2024.240057
|
Reconfigurable optical neural networks with Plug-and-Play metasurfaces
-
Abstract
In a very recent study, Prof. Lingling Huang and co-workers proposed and demonstrated reconfigurable optical neural networks based on cascaded metasurfaces. By fixing one metasurface and switching the other pluggable metasurfaces, the neural networks, which operate at near-infrared wavelengths, can perform distinct recognition tasks for handwritten digits and fashion products. This innovative device opens up an avenue for all-optical, high-speed, low-power, and multi-functional artificial intelligence systems. -
-
References
[1] Aggarwal CC. Neural Networks and Deep Learning (Springer, Cham, 2018). [2] Wetzstein G, Ozcan A, Gigan S et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39–47 (2020). doi: 10.1038/s41586-020-2973-6 [3] Shastri BJ, Tait AN, de Lima TF et al. Photonics for artificial intelligence and neuromorphic computing. Nat Photonics 15, 102–114 (2021). doi: 10.1038/s41566-020-00754-y [4] Shen YC, Harris NC, Skirlo S et al. Deep learning with coherent nanophotonic circuits. Nat Photonics 11, 441 (2017). doi: 10.1038/nphoton.2017.93 [5] Ashtiani F, Geers AJ, Aflatouni F. An on-chip photonic deep neural network for image classification. Nature 606, 501–506 (2022). doi: 10.1038/s41586-022-04714-0 [6] Lin X, Rivenson Y, Yardimci NT et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018). doi: 10.1126/science.aat8084 [7] Luo Y, Mengu D, Yardimci NT et al. Design of task-specific optical systems using broadband diffractive neural networks. Light Sci Appl 8, 112 (2019). doi: 10.1038/s41377-019-0223-1 [8] Luo XH, Hu YQ, Ou XN et al. Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible. Light Sci Appl 11, 158 (2022). doi: 10.1038/s41377-022-00844-2 [9] He C, Zhao D, Fan F et al. Pluggable multitask diffractive neural networks based on cascaded metasurfaces. Opto-Electron Adv 7, 230005 (2024). doi: 10.29026/oea.2024.230005 [10] Ma W, Xu YH, Xiong B et al. Pushing the limits of functionality-multiplexing capability in metasurface design based on statistical machine learning. Adv Mater 34, 2110022 (2022). doi: 10.1002/adma.202110022 [11] Xiong B, Liu Y, Xu YH et al. Breaking the limitation of polarization multiplexing in optical metasurfaces with engineered noise. Science 379, 294–299 (2023). doi: 10.1126/science.ade5140 -
Access History
Figures(1)
Article Metrics
Export File
Citation
Liu YM, Li YX. Reconfigurable optical neural networks with Plug-and-Play metasurfaces. Opto-Electron Adv 7, 240057 (2024). doi: 10.29026/oea.2024.240057
Format
Content
DownLoad:
-
Figure 1.
(a) Schematic illustration of P-DNNs, which can perform recongnization of handwritten digits and fashion by switching the pluggable classification layer. The inset at the bottom right corner shows the scanning electron micrograph of the fabricated metasurface. (b) Characterization and performance of P-DNNs. Left column: handwritten digits and fashion input images. Middle column: experimentally detected energy distribution maps for handwritten digits and fashion. Right column: experimental and simulation results of energy distribution for handwritten digits and fashion. ΔE represents the difference between the percentage of maximum and second maximum energy. The figures are adapted from ref.8 with modification.
