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Publication

Growth-interruption-induced low-density InAs quantum dots on GaAs

Blandine Alloing
2008
Journal Articles

Abstract

We investigate the use of growth interruption to obtain low-density InAs quantum dots (QDs) on GaAs. The process was realized by Ostwald-type ripening of a thin InAs layer. It was found that the optical properties of the QDs as a function of growth interruption strongly depend on InAs growth rate. By using this approach, a low density of QDs (4 dots/mu m(2)) with uniform size distribution was achieved. As compared to QDs grown without growth interruption, a larger energy separation between the QD confined levels was observed, suggesting a situation closer to the ideal zero-dimensional system. Combining with an InGaAs capping layer such as In-rich QDs enable 1.3 mu m emission at 4 K. (C) 2008 American Institute of Physics. [DOI: 10.1063/1.3000483]

Official source

https://infoscience.epfl.ch/entities/publication/4ef6050d-3639-482b-bb24-e104a07e2f6d

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Blandine Alloing
2008
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/4ef6050d-3639-482b-bb24-e104a07e2f6d

About this result

Related concepts (14)

Quantum dot

Quantum dots (QDs) – also called semiconductor nanocrystals, are semiconductor particles a few nanometres in size, having optical and electronic properties that differ from those of larger particles as a result of quantum mechanics. They are a central topic in nanotechnology and materials science. When the quantum dots are illuminated by UV light, an electron in the quantum dot can be excited to a state of higher energy. In the case of a semiconducting quantum dot, this process corresponds to the transition of an electron from the valence band to the conductance band.

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In probability theory and statistics, the continuous uniform distributions or rectangular distributions are a family of symmetric probability distributions. Such a distribution describes an experiment where there is an arbitrary outcome that lies between certain bounds. The bounds are defined by the parameters, and which are the minimum and maximum values. The interval can either be closed (i.e. ) or open (i.e. ). Therefore, the distribution is often abbreviated where stands for uniform distribution.

Discrete uniform distribution

In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution wherein a finite number of values are equally likely to be observed; every one of n values has equal probability 1/n. Another way of saying "discrete uniform distribution" would be "a known, finite number of outcomes equally likely to happen". A simple example of the discrete uniform distribution is throwing a fair die. The possible values are 1, 2, 3, 4, 5, 6, and each time the die is thrown the probability of a given score is 1/6.

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