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Machine learning pattern recognition in integrated silicon photonics design

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1109/PN50013.2020.9166993
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Affiliation
  1. National Research Council of Canada. Advanced Electronics and Photonics
  2. National Research Council of Canada. Digital Technologies
FormatText, Article
Conference2020 Photonics North (PN), May 26-28, 2020, Niagara Falls, Ontario
Physical description1 p.
Subjectnanophotonics; silicon photonics; optimization; machine learning; pattern recognition; principal component analysis (PCA); dimensionality reduction; inverse design
Abstract
The optimization of complex high-dimensional photonic structures is often limited by computational resources. Current techniques based on global optimization algorithms or shape/topology inverse design treat design variables as entirely independent. However, there is often correlation between the input variables and patterns in the design outcomes. We review our strategy of using machine learning pattern recognition for building the performance map of a high-dimensional design space, thereby quickly guiding the search to a small region of interest and significantly improving the computational efficiency. This strategy is found beneficial in both forward and inverse design process flow.
Publication date
PublisherIEEE
In
Series
  • Photonics North.
LanguageEnglish
Peer reviewedYes
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Record identifier9d9404b9-7fbf-4d34-937d-b5abeb4d8fcb
Record created2021-07-21
Record modified2021-07-23
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