Periodic gratings with complex shapes are designed by machine learning as emitters for a thermophotovoltaic system with a GaSb photovoltaic absorber operating at 1000K in order to maximize thermophotovoltaic efficiency. We also demonstrate complex-shaped GaN grating VCSEL mirrors designed by machine learning for high reflectivity and a wide stopband with center wavelength of 500nm. The results obtained indicate a 50% increase in stopbandwidth for the complex-shaped grating over that of a rectangular grating.
Controlled growth and increased radiative recombination rates of InGaN quantum dots (QDs) are demonstrated. The InGaN QDs are grown by a self-assembly (SA) method using metal-organic chemical vapor deposition on planar GaN and photoelectrochemically (PEC) etched InGaN QD templates. The PEC QD templates are formed from InGaN layers with a coherent source, which results in controlled QD sizes, and are capped and planarized with AlGaN/GaN layers. The PEC QDs behave as seeds via localizing strain near the QDs, which provide improved control of the SA QD growth. The SA QDs grown on PEC QD templates are smaller and have controlled sizes. Multiple quantum dots, consisting of 4 periods of SA QDs and AlGaN/GaN barrier layers, grown on PEC QD templates have higher radiative recombination rates. The higher rates are a result of the controlled and smaller-sized SA QDs. Forming SA QDs on PEC QDs templates can enhance the performance of InGaN-based QD emitters.
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