Analysing radiative and non-radiative recombination in InAs QDs on Si for integrated laser applications

Jonathan R. Orchard; Chris Woodhead; Samuel Shutts; Jiang Wu; Angela Sobiesierski; Rob J. Young; Richard Beanland; Huiyun Liu; Peter M. Smowton; David J. Mowbray

doi:10.1117/12.2209693

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15 March 2016 Analysing radiative and non-radiative recombination in InAs QDs on Si for integrated laser applications

Jonathan R. Orchard, Chris Woodhead, Samuel Shutts, Jiang Wu, Angela Sobiesierski, Rob J. Young, Richard Beanland, Huiyun Liu, Peter M. Smowton, David J. Mowbray

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Proceedings Volume 9758, Quantum Dots and Nanostructures: Growth, Characterization, and Modeling XIII; 975809 (2016) https://doi.org/10.1117/12.2209693
Event: SPIE OPTO, 2016, San Francisco, California, United States

Abstract

Three InAs quantum dot (QD) samples with dislocation filter layers (DFLs) are grown on Si substrates with and without in-situ annealing. Comparison is made to a similar structure grown on a GaAs substrate. The three Si grown samples have different dislocation densities in their active region as revealed by structural studies. By determining the integrated emission as a function of laser power it is possible to determine the power dependence of the radiative efficiency and compare this across the four samples. The radiative efficiency increases with decreasing dislocation density; this also results in a decrease in the temperature quenching of the PL. A laser structures grown on Si and implementing the same optimum DFL and annealing procedure exhibits a greater than 3 fold reduction in threshold current as well as a two fold increase in slope efficiency in comparison to a device in which no annealing is applied.

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Jonathan R. Orchard, Chris Woodhead, Samuel Shutts, Jiang Wu, Angela Sobiesierski, Rob J. Young, Richard Beanland, Huiyun Liu, Peter M. Smowton, and David J. Mowbray "Analysing radiative and non-radiative recombination in InAs QDs on Si for integrated laser applications", Proc. SPIE 9758, Quantum Dots and Nanostructures: Growth, Characterization, and Modeling XIII, 975809 (15 March 2016); https://doi.org/10.1117/12.2209693

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