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Holography in artificial neural networks
Open accessAbstract
The dense interconnections that characterize neural networks are most readily implemented using optical signal processing. Optoelectronic 'neurons' fabricated from semiconducting materials can be connected by holographic images recorded in photorefractive crystals. Processes such as learning can be demonstrated using holographic optical neural networks.
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Cited by
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
Orbital Angular Momentum Holography for High-Security Encryption
Fang, X., Ren, H., & Gu, M. (2020) · Nature Photonics · doi:10.1038/s41566-019-0560-x · Cited by 894
Mengu, D., Luo, Y., Rivenson, Y., & Ozcan, A. (2020) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2019.2921376 · Cited by 255
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
- Psaltis, D., Brady, D., Gu, X.-G., & Lin, S.
- Venue
- Nature
- Published
- 1990-01
- Volume
- 343
- Issue
- 6256
- Pages
- 325-330
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2028891317
- Cited by
- 279
- References
- 0
- FWCI
- 13.45
- Type
- journal article
- Language
- en
- Open access
- green
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