The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Fourier-space Diffractive Deep Neural Network
Abstract
In this Letter we propose the Fourier-space diffractive deep neural network ($\mathrm{F}\text{\ensuremath{-}}\mathrm{D}^{2}\mathrm{NN}$) for all-optical image processing that performs advanced computer vision tasks at the speed of light. The $\mathrm{F}\text{\ensuremath{-}}\mathrm{D}^{2}\mathrm{NN}$ is achieved by placing the extremely compact diffractive modulation layers at the Fourier plane or both Fourier and imaging planes of an optical system, where the optical nonlinearity is introduced from ferroelectric thin films. We demonstrated that $\mathrm{F}\text{\ensuremath{-}}\mathrm{D}^{2}\mathrm{NN}$ can be trained with deep learning algorithms for all-optical saliency detection and high-accuracy object classification.
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Self-citationWide-field, high-resolution Fourier ptychographic microscopy
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Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence
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Self-citationHigh-Efficiency Transmissive Tunable Metasurfaces for Binary Cascaded Diffractive Layers
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Metadata
- Authors
- Yan, T., Wu, J., Zhou, T., Xie, H., Xu, F., Fan, J., Fang, L., Lin, X., & Dai, Q.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2019-07-09
- Volume
- 123
- Issue
- 2
- Pages
- 023901-023901
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2961079966
- Cited by
- 408
- References
- 33
- FWCI
- 13.81
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yan, T., Wu, J., Zhou, T., Xie, H., Xu, F., Fan, J., Fang, L., Lin, X., & Dai, Q. (2019). Fourier-space Diffractive Deep Neural Network. Physical Review Letters. https://doi.org/10.1103/physrevlett.123.023901
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