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Advancing silicon photonics through machine learning: from device design to fabrication

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1109/NANO61778.2024.10628655
AuthorSearch for: 1ORCID identifier: https://orcid.org/0000-0002-0490-5224; Search for: ORCID identifier: https://orcid.org/0000-0001-9323-4452; Search for: 2ORCID identifier: https://orcid.org/0000-0003-3349-1590; Search for:
Affiliation
  1. National Research Council Canada. Quantum and Nanotechnologies
  2. National Research Council Canada. Digital Technologies
FormatText, Article
Conference2024 IEEE 24th International Conference on Nanotechnology (NANO), July 8-11, 2024, Gijon, Spain
Subjectnanophotonics; silicon photonics; inverse design; machine learning; fabrication; performance evaluation; machine learning algorithms; navigation; design methodology
Abstract
The advances in fabrication technologies have enabled the development of new nanophotonic devices, utilizing improved structural resolution for greater integration density and functionality. The complexity of these devices necessitates the adoption of algorithm-assisted design methods to navigate the vast design space. This evolution presents a push-pull dynamic: while striving for advanced device functionalities, the limits of fabrication technology are tested, potentially affecting device robustness. Our research leverages machine learning to address this challenge, balancing the drive for innovation with the practicalities of manufacturing. This paper outlines our strategies to optimize device design and predict fabrication outcomes, improving both the performance and robustness of next-generation silicon photonic devices.
Publication date
PublisherIEEE
In
  • 2024 IEEE 24th International Conference on Nanotechnology (NANO) (8 July 2024): 460–463.
LanguageEnglish
Peer reviewedYes
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Record identifierc15f8444-a20d-43a3-b0e7-3855de41d041
Record created2024-11-14
Record modified2024-11-15
Date modified: