| DOI | Trouver le DOI : https://doi.org/10.1109/PN56061.2022.9908358 |
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| Auteur | Rechercher : Gostimirovic, Dusan; Rechercher : Xu, Dan-Xia1; Rechercher : Liboiron-Ladouceur, Odile; Rechercher : Grinberg, Yuri2 |
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| Affiliation | - Conseil national de recherches du Canada. Électronique et photonique avancées
- Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 2022 Photonics North (PN), May 24-26, 2022, Niagara Falls, ON, Canada |
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| Sujet | silicon photonics; machine learning; deep learning; convolutional neural networks; fabrication process variations; fabrication; performance evaluation; degradation; scanning electron microscopy; electric potential; predictive models |
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| Résumé | Next-generation silicon photonic devices often contain complex, nanoscale features to enhance their performance; however, these features experience significant variations from fabrication imperfections, which cause significant performance degradation in practical implementation. We present a machine learning model that learns from a modest set of scanning electron microscope images to quickly and accurately predict the fabrication variations. Additionally, we present the potential use of this model in the automated correction of fabrication-sensitive device features. |
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| Date de publication | 2022-05-24 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 366e51cb-c888-4f15-89f6-d08cd375ec64 |
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| Enregistrement créé | 2023-01-24 |
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| Enregistrement modifié | 2023-01-26 |
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