Berlin 2024 – wissenschaftliches Programm
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HL: Fachverband Halbleiterphysik
HL 1: Nitrides: Preparation and characterization I
HL 1.6: Vortrag
Montag, 18. März 2024, 10:45–11:00, EW 015
deciphering the origin of small pits in GaInN/GaN quantum wells structures: correlation of defect formation and growth conditions — •Mahdi Khalili Hezarjaribi1,2, Rodrigo de Vasconcellos Lourenço1,2, Uwe Rossow1,2, Heiko Bremers1,2, and Andreas Hangleiter1,2 — 1Institute of Applied Physics, Technische Universität Braunschweig, Germany — 2Laboratory for Emerging Nanometrology, Braunschweig, Germany
Threading dislocations are non-radiative centers and can affect the luminescence efficiency of light emitters based on GaInN/GaN quantum wells (QWs).Specially for heteroepitaxial growth,i.e on sapphire substrate, the dislocation density is high, leading to a significantly reduced luminescence efficiency. This issue can be addressed by intentionally creating V-pits, screening the threading dislocations cores. We have observed that sometimes additional pits are visible after the growth of the quantum well stack. Those pits are far smaller in size and usually appear as pairs, and may be associated to defects, e.g. stacking faults, which are formed during QW growth and may act as non-radiative centers. Employing scanning electron microscope (SEM) imagery, we investigate the V-pits. The analysis of thousands of images is a very time consuming and laborious procedure. We developed a model, using YOLO machine learning algorithm, which can objectively characterize the pits,their size distribution, density, and can selectively focus on the small pits.The trained model is more efficient and faster than conventional methods.Using the results and systematic variations of structural parameters, we elucidate the origin of the small pits.
Keywords: machine learning; nitrides; dislocation; pit; quantum well