Robert F. H. Hunter,1 Gavin P. Forcade,1 Yuri Grinberg,2 Meghan N. Beattie,1 D. Paige Wilson,1 Christopher E. Valdivia,1 Mathieu de Lafontaine,1 Louis-Philippe St-Arnaud,1 Henning Helmershttps://orcid.org/0000-0003-1660-7651,3 Oliver Höhn,3 David Lackner,3 Carmine Pellegrino,3 Jacob J. Krich,1 Alexandre W. Walker,2 Karin Hinzer1
1Univ. of Ottawa (Canada) 2National Research Council Canada (Canada) 3Fraunhofer-Institut für Solare Energiesysteme ISE (Germany)
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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, 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