DOI | Resolve DOI: https://doi.org/10.1109/GFP51802.2021.9673932 |
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Author | Search for: Mokeddem, Zindine; Search for: Melati, Daniele; Search for: Gonzalez-Andrade, David; Search for: Duong Dinh, Thi Thuy; Search for: Montesinos-Ballester, Miguel; Search for: Cassan, Eric; Search for: Marris-Morini, Delphine; Search for: Grinberg, Yuri1; Search for: Cheben, Pavel2; Search for: Xu, Dan-Xia2; Search for: Schmid, Jens2; Search for: Vivien, Laurent; Search for: Velasco, Aitor V.; Search for: Alonso-Ramos, Carlos |
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Affiliation | - National Research Council of Canada. Digital Technologies
- National Research Council of Canada. Advanced Electronics and Photonics
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Funder | Search for: Agence Nationale de la Recherche |
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Format | Text, Article |
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Conference | 2021 IEEE 17th International Conference on Group IV Photonics (GFP), December 7-10, 2021, Malaga, Spain |
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Physical description | 2 p. |
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Subject | spectrometer; Fourier-transform; deep-learning; silicon photonics |
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Abstract | Silicon photonics spectrometers have great potential for applications in medicine and hazard detection. However, silicon spectrometers are very sensitive to fabrication imperfections and environmental conditions. Here, we study the use of deep-learning algorithms to improve tolerance of Fourier-transform spectrometers against fabrication imperfections and temperature variations. |
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Publication date | 2021-12-07 |
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Publisher | IEEE |
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In | |
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Language | English |
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Peer reviewed | Yes |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | d1110875-62d8-447a-95f1-0860213515c5 |
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Record created | 2022-05-06 |
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Record modified | 2022-05-09 |
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