Photonic Neural Networks Based on Integrated Silicon Microresonators
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2024/01/01
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Journal
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Indian UGC (journal)
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Refrences150
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Citations2
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Stefano Biasi Nanoscience Laboratory, Department of Physics, University of Trento, Trento, Italy.
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Giovanni Donati Nanoscience Laboratory, Department of Physics, University of Trento, Trento, Italy.
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Alessio Lugnan Nanoscience Laboratory, Department of Physics, University of Trento, Trento, Italy.
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Mattia Mancinelli Nanoscience Laboratory, Department of Physics, University of Trento, Trento, Italy.
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Emiliano Staffoli Nanoscience Laboratory, Department of Physics, University of Trento, Trento, Italy.
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Lorenzo Pavesi Nanoscience Laboratory, Department of Physics, University of Trento, Trento, Italy. ORCID
Abstract
Cite
Biasi, Stefano, et al. “Photonic Neural Networks Based on Integrated Silicon Microresonators”. Intelligent Computing, vol. 3, 2024, https://doi.org/10.34133/icomputing.0067.
Biasi, S., Donati, G., Lugnan, A., Mancinelli, M., Staffoli, E., & Pavesi, L. (2024). Photonic Neural Networks Based on Integrated Silicon Microresonators. Intelligent Computing, 3. https://doi.org/10.34133/icomputing.0067
Biasi, Stefano, Giovanni Donati, Alessio Lugnan, Mattia Mancinelli, Emiliano Staffoli, and Lorenzo Pavesi. “Photonic Neural Networks Based on Integrated Silicon Microresonators”. Intelligent Computing 3 (2024). https://doi.org/10.34133/icomputing.0067.
Biasi S, Donati G, Lugnan A, Mancinelli M, Staffoli E, Pavesi L. Photonic Neural Networks Based on Integrated Silicon Microresonators. Intelligent Computing. 2024;3.
Journal Category
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Photonic binary convolutional neural network based on microring resonator array | IEEE Photonics Technology Letters |
| 2023 | |
| Equalization of a 10 gbps imdd signal by a small silicon photonics time delayed neural network | 2023 | |||
Human-level play in the game of
Diplomacy
by combining language models with strategic reasoning | Science |
| 40 | 2022 |
| Interferometric cavity ringdown technique for ultrahigh Q-factor microresonators | Optics Letters |
| 2022 | |
| Characterizing coherent integrated photonic neural networks under imperfections | 2022 |
Refrences Analysis
The category
Science: Physics 80
is the most frequently represented among the references in this article. It primarily includes studies from Optics Express and Scientific Reports. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Decoding Epileptic Seizures: Exploring In Vitro Approaches to Unravel Pathophysiology and Propel Future Therapeutic Breakthroughs | Biomedical Materials & Devices | 2024 | ||
Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review | Nanomaterials |
| 2024 |
Citations Analysis
The category
Science: Chemistry 1
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review and was published in 2024. The most recent citation comes from a 2024 study titled Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review. This article reached its peak citation in 2024, with 2 citations. It has been cited in 2 different journals, 50% of which are open access. Among related journals, the Nanomaterials cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.