The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
All-optical neural network with nonlinear activation functions
Open accessAbstract
Artificial neural networks (ANNs) have now been widely used for industry applications and also played more important roles in fundamental researches. Although most ANN hardware systems are electronically based, optical implementation is particularly attractive because of its intrinsic parallelism and low energy consumption. Here, we propose and demonstrate fully-functioned all optical neural networks (AONNs), in which linear operations are programmed by spatial light modulators and Fourier lenses, and optical nonlinear activation functions are realized with electromagnetically induced transparency in laser-cooled atoms. Moreover, all the errors from different optical neurons here are independent, thus the AONN could scale up to a larger system size with final error still maintaining in a similar level of a single neuron. We confirm its capability and feasibility in machine learning by successfully classifying the order and disorder phases of a typical statistic Ising model. The demonstrated AONN scheme can be used to construct various ANNs of different architectures with the intrinsic parallel computation at the speed of light.
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Cites
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BackgroundElectromagnetically Induced Transparency
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Deep learning with coherent nanophotonic circuits
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All-optical machine learning using diffractive deep neural networks
Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018) · Science · doi:10.1126/science.aat8084 · Cited by 2,568
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All-optical spiking neurosynaptic networks with self-learning capabilities
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Cited by
Photonics for artificial intelligence and neuromorphic computing
Shastri, B. J., Tait, A. N., Ferreira de Lima, T., Pernice, W. H. P., Bhaskaran, H., Wright, C. D., & Prucnal, P. R. (2021) · Nature Photonics · doi:10.1038/s41566-020-00754-y · Cited by 1,756
An on-chip photonic deep neural network for image classification
Ashtiani, F., Geers, A. J., & Aflatouni, F. (2022) · Nature · doi:10.1038/s41586-022-04714-0 · Cited by 699
A programmable diffractive deep neural network based on a digital-coding metasurface array
Liu, C., Ma, Q., Luo, Z. J., Hong, Q. R., Xiao, Q., Zhang, H. C., Miao, L., Yu, W. M., Cheng, Q., Li, L., et al. (2022) · Nature Electronics · doi:10.1038/s41928-022-00719-9 · Cited by 633
Gao, S., Chen, H., Wang, Y., Duan, Z., Zhang, H., Sun, Z., Shen, Y., & Lin, X. (2024) · Light: Science & Applications · doi:10.1038/s41377-024-01511-4 · Cited by 32
High-Efficiency Transmissive Tunable Metasurfaces for Binary Cascaded Diffractive Layers
Jia, Y., Lu, H., Fan, Z., Wu, B., Qu, F., Zhao, M.-J., Qian, C., & Chen, H. (2024) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2024.3385112 · Cited by 32
Metadata
- Authors
- Zuo, Y., Li, B., Zhao, Y., Jiang, Y., Chen, Y.-C., Chen, P., Jo, G.-B., Liu, J., & Du, S.
- Venue
- Optica
- Publisher
- Optica Publishing Group
- Published
- 2019-08-29
- Volume
- 6
- Issue
- 9
- Pages
- 1132
- ISSN
- 2334-2536 (ISSN-L)
- OpenAlex
- W2970080722
- Cited by
- 474
- References
- 33
- FWCI
- 19.69
- Type
- journal article
- Language
- en
- Open access
- gold
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