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9 March 2024 Using machine learning to optimize multi-junction photonic power converters
Robert F. H. Hunter, Gavin P. Forcade, Yuri Grinberg, Meghan N. Beattie, D. Paige Wilson, Christopher E. Valdivia, Mathieu de Lafontaine, Louis-Philippe St-Arnaud, Henning Helmers, Oliver Höhn, David Lackner, Carmine Pellegrino, Jacob J. Krich, Alexandre W. Walker, Karin Hinzer
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Proceedings Volume PC12881, Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XIII; PC1288109 (2024) https://doi.org/10.1117/12.3002658
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
We have developed a machine learning empowered computational framework to facilitate design space exploration for optoelectronic devices. In this work, we apply dimensionality reduction and clustering machine learning algorithms to identify optimal ten-junction C-band photonic power converter (PPC) designs. We outline our framework, design optimization procedure, calibrated optoelectronic model, and experimental calibration devices. We report on top performing device designs for on-substrate and flat back-reflector architectures. We comment on the design sensitivity for these PPCs and on the applicability of dimensionality reduction and clustering algorithms to assist in optoelectronic device design.
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Robert F. H. Hunter, Gavin P. Forcade, Yuri Grinberg, Meghan N. Beattie, D. Paige Wilson, Christopher E. Valdivia, Mathieu de Lafontaine, Louis-Philippe St-Arnaud, Henning Helmers, Oliver Höhn, David Lackner, Carmine Pellegrino, Jacob J. Krich, Alexandre W. Walker, and Karin Hinzer "Using machine learning to optimize multi-junction photonic power converters", Proc. SPIE PC12881, Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XIII, PC1288109 (9 March 2024); https://doi.org/10.1117/12.3002658
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KEYWORDS
Design and modelling
Machine learning
Optoelectronic devices
Pattern recognition
Performance modeling
Photovoltaics
Principal component analysis
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