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.
Photonic power converters (PPCs) are photovoltaic cells that convert monochromatic light into electric power. The impact of luminescent coupling (LC) on InGaAs-based PPCs is studied. Multi-junction PPCs are simulated using an experimentally validated drift-diffusion model, and the contribution of LC is quantified. Up to 85% of the photons emitted across the InGaAs layers are re-absorbed in the dual-junction device considered. This number increases to 96% when a back reflector is included due to improved light management. Interference effects produced by multiple reflections are examined as a function of the emission angle.
Photonic power converters designed to operate in the telecommunications O-band were measured under non-uniform 1319 nm laser illumination. Two device architectures were studied, based on lattice-matched InGaAsP on an InP substrate and lattice-mismatched InGaAs grown on GaAs using a metamorphic buffer. The maximum measured efficiencies were 52.9% and 48.8% for the lattice-matched and -mismatched designs respectively. Both 5.4-mm2 devices were insensitive to the incident laser spot size for input powers of < 250 mW and exhibited better performance for larger spot sizes with more uniform illumination profiles at higher powers.
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