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Literature DB >> 2300184

Holography in artificial neural networks.

Abstract

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.

Mesh：

Year: 1990 PMID： 2300184 DOI： 10.1038/343325a0

Source DB: PubMed Journal: Nature ISSN： 0028-0836 Impact factor: 49.962

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Cited

11 in total

1. High-performance holographic technologies for fluid-dynamics experiments.

Authors: Sergei S Orlov; Snezhana I Abarzhi; Se Baek Oh; George Barbastathis; Katepalli R Sreenivasan
Journal: Philos Trans A Math Phys Eng Sci Date: 2010-04-13 Impact factor: 4.226

Review 2. Inference in artificial intelligence with deep optics and photonics.

Authors: Gordon Wetzstein; Aydogan Ozcan; Sylvain Gigan; Shanhui Fan; Dirk Englund; Marin Soljačić; Cornelia Denz; David A B Miller; Demetri Psaltis
Journal: Nature Date: 2020-12-02 Impact factor: 49.962

3. Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks.

Authors: Deniz Mengu; Yi Luo; Yair Rivenson; Aydogan Ozcan
Journal: IEEE J Sel Top Quantum Electron Date: 2019-06-06 Impact factor: 4.544

4. Materials for emergent silicon-integrated optical computing.

Authors: Alexander A Demkov; Chandrajit Bajaj; John G Ekerdt; Chris J Palmstrøm; S J Ben Yoo
Journal: J Appl Phys Date: 2021-08-19 Impact factor: 2.877

5. Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network.

Authors: Jingxi Li; Yi-Chun Hung; Onur Kulce; Deniz Mengu; Aydogan Ozcan
Journal: Light Sci Appl Date: 2022-05-26 Impact factor: 20.257

Holography in artificial neural networks.

1. High-performance holographic technologies for fluid-dynamics experiments.

Review 2. Inference in artificial intelligence with deep optics and photonics.

3. Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks.

4. Materials for emergent silicon-integrated optical computing.

5. Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network.

6. Artificial neural networks enabled by nanophotonics.

7. All-optical information-processing capacity of diffractive surfaces.

8. Spectrally encoded single-pixel machine vision using diffractive networks.

9. Space-efficient optical computing with an integrated chip diffractive neural network.

Review 10. Photonics enabled intelligence system to identify SARS-CoV 2 mutations.