Mapping the global design space of nanophotonic components using machine learning pattern recognition

Daniele Melati, Yuri Grinberg, Mohsen Kamandar Dezfouli, Siegfried Janz, Pavel Cheben, Jens H. Schmid, Alejandro Sánchez-Postigo and Dan-Xia Xu ()
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Daniele Melati: Advanced Electronics and Photonics Research Centre, National Research Council Canada
Yuri Grinberg: Digital Technologies Research Centre, National Research Council Canada
Mohsen Kamandar Dezfouli: Advanced Electronics and Photonics Research Centre, National Research Council Canada
Siegfried Janz: Advanced Electronics and Photonics Research Centre, National Research Council Canada
Pavel Cheben: Advanced Electronics and Photonics Research Centre, National Research Council Canada
Jens H. Schmid: Advanced Electronics and Photonics Research Centre, National Research Council Canada
Alejandro Sánchez-Postigo: Universidad de Málaga, Departamento de Ingeniería de Comunicaciones, ETSI Telecomunicación, Campus de Teatinos s/n
Dan-Xia Xu: Advanced Electronics and Photonics Research Centre, National Research Council Canada

Nature Communications, 2019, vol. 10, issue 1, 1-9

Abstract: Abstract Nanophotonics finds ever broadening applications requiring complex components with many parameters to be simultaneously designed. Recent methodologies employing optimization algorithms commonly focus on a single performance objective, provide isolated designs, and do not describe how the design parameters influence the device behaviour. Here we propose and demonstrate a machine-learning-based approach to map and characterize the multi-parameter design space of nanophotonic components. Pattern recognition is used to reveal the relationship between an initial sparse set of optimized designs through a significant reduction in the number of characterizing parameters. This defines a design sub-space of lower dimensionality that can be mapped faster by orders of magnitude than the original design space. The behavior for multiple performance criteria is visualized, revealing the interplay of the design parameters, highlighting performance and structural limitations, and inspiring new design ideas. This global perspective on high-dimensional design problems represents a major shift in modern nanophotonic design and provides a powerful tool to explore complexity in next-generation devices.

Date: 2019
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DOI: 10.1038/s41467-019-12698-1

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