| DOI | Resolve DOI: https://doi.org/10.1109/PN56061.2022.9908358 |
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| Author | Search for: Gostimirovic, Dusan; Search for: Xu, Dan-Xia1; Search for: Liboiron-Ladouceur, Odile; Search for: Grinberg, Yuri2 |
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| Affiliation | - National Research Council of Canada. Advanced Electronics and Photonics
- National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 2022 Photonics North (PN), May 24-26, 2022, Niagara Falls, ON, Canada |
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| Subject | 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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| Abstract | 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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| Publication date | 2022-05-24 |
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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 | 366e51cb-c888-4f15-89f6-d08cd375ec64 |
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| Record created | 2023-01-24 |
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| Record modified | 2023-01-26 |
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