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28 April 2017 Growth scheme for quantum dots with low fine structure splitting at telecom wavelengths (Conference Presentation)
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Abstract
Quantum dots based on InAs/InP hold the promise to deliver entangled photons with wavelength suitable for the standard telecom window around 1550 nm, which makes them predestined to be used in future quantum networks applications based on existing fiber optics infrastructure. A prerequisite for the generation of such entangled photons is a small fine structure splitting (FSS) in the quantum dot excitonic eigenstates, as well as the ability to integrate the dot into photonic structures to enhance and direct its emission. Using optical spectroscopy, we show that a growth strategy based on droplet epitaxy can simultaneously address both issues. Contrary to the standard Stranski-Krastanow technique, droplet epitaxy dots do not rely on material strains during growth, which results in a drastic improvement in dot symmetry. As a consequence, the average exciton FSS is reduced by more than a factor 4, which in fact makes all the difference between easily finding a dot with the required FSS and not finding one at all. Furthermore, we demonstrate that droplet epitaxy dots can be grown on the necessary surface (001) for high quality optical microcavities, which increases dot emission count rates by more than a factor of five. Together, these properties make droplet epitaxy quantum dots readily suitable for the generation of entangled photons at telecom wavelengths.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tina Muller, Joanna Skiba-Szymanska, R. Mark Stevenson, Christiana Varnava, Martin Felle, Jan Huwer, Ian Farrer, Andrey B. Krysa, Peter Spencer, David A. Ritchie, Jon Heffernan, and Andrew J. Shields "Growth scheme for quantum dots with low fine structure splitting at telecom wavelengths (Conference Presentation)", Proc. SPIE 10114, Quantum Dots and Nanostructures: Growth, Characterization, and Modeling XIV, 101140O (28 April 2017); https://doi.org/10.1117/12.2252159
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