Reaping the benefits of machine learning pattern recognition in nanophotonic component design

Yuri Grinberg; Daniele Melati; Mohsen Kamandar Dezfouli; Siegfried Janz; Jens H. Schmid; Pavel Cheben; Alejandro Sánchez-Postigo; J. Gonzalo Wangüemert-Pérez; Iñigo Molina-Fernández; Alejandro Ortega-Moñux; Dan-Xia Xu

doi:10.1117/12.2506787

4 March 2019 Reaping the benefits of machine learning pattern recognition in nanophotonic component design

Yuri Grinberg, Daniele Melati, Mohsen Kamandar Dezfouli, Siegfried Janz, Jens H. Schmid, Pavel Cheben, Alejandro Sánchez-Postigo, J. Gonzalo Wangüemert-Pérez, Iñigo Molina-Fernández, Alejandro Ortega-Moñux, Dan-Xia Xu

Author Affiliations +

Proceedings Volume 10921, Integrated Optics: Devices, Materials, and Technologies XXIII; 109210B (2019) https://doi.org/10.1117/12.2506787
Event: SPIE OPTO, 2019, San Francisco, California, United States

Abstract

Integrated nanophotonic component design processes are often constrained by computational resources. Advances in simulation and optimization tools have allowed more efficient exploration of larger design spaces. These developments reduce the time-consuming and intuition-limited effort of encoding physical insights into the design structure. However, we argue that efficient optimization is only part of the solution to tackle larger multi-parameter design spaces. Finding patterns in such a space can be more valuable than identifying the individual optima alone. This is particularly true when transitioning from simulation to real device fabrication, where considerations such as tolerance to fabrication imperfections, bandwidth, etc. take an important role but are ignored at the optimization stage. The elucidation of patterns in a complex design space enables efficient identification of designs addressing these additional considerations. As an example, in this presentation we demonstrate how limited data collected from the optimization process of a multisegment vertical grating coupler can be used to identify such patterns through the application of machine learning techniques. The identified patterns, some more interpretable than others, can be used in multiple ways: from speeding up the remaining optimization process itself to gaining insight into the properties of an interesting subset of designs. Together those insights offer a significantly clearer picture of the design space and form the basis for making much more informed decisions on the final designs to be fabricated.

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Yuri Grinberg, Daniele Melati, Mohsen Kamandar Dezfouli, Siegfried Janz, Jens H. Schmid, Pavel Cheben, Alejandro Sánchez-Postigo, J. Gonzalo Wangüemert-Pérez, Iñigo Molina-Fernández, Alejandro Ortega-Moñux, and Dan-Xia Xu "Reaping the benefits of machine learning pattern recognition in nanophotonic component design", Proc. SPIE 10921, Integrated Optics: Devices, Materials, and Technologies XXIII, 109210B (4 March 2019); https://doi.org/10.1117/12.2506787

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