Capping of InAs/GaAs Quantum Dots for GaAs Based Lasers
Nowadays, self-assembled InAs quantum dots grown on GaAs substrates are intensively studied due to their potential applications: quantum dot ensembles are employed in laser active regions and mid-infrared detectors, single quantum dots have a potential for application in memories, two-level information processing or single photon sources (Henini& Bugajski, 2005; Mowbray& Skolnick, 2005; Battacharya, 2007).
Type I–type II band alignment of a GaAsSb/InAs/GaAs quantum dot heterostructure influenced by dot size and strain-reducing layer composition
The aim of this work is to red shift quantum dot (QD) photoluminescence (PL) towards telecommunication wavelengths by engineering the metalorganic vapour phase epitaxy (MOVPE) prepared structure of InAs/GaAs QDs covered by a GaAsSb strain-reducing layer. Our results proved that type I or type II band alignment can be controlled by both GaAsSb composition and QD size. Maintaining type I heterostructure is important for high luminescence efficiency and emission wavelength stability of the QD structure. The simulation of electron structure in InAs QDs covered with a GaAsSb strain-reducing layer as well as experimental results suggest the importance of increasing QD size for obtaining a longer wavelength PL from the type I heterostructure. The PL maximum wavelength 1371 nm was achieved for the MOVPE prepared type I QD structure with 14% of Sb in GaAsSb. This type of structure exhibits seven times higher PL intensity, twice narrower PL peak and 85 meV redshift in comparison with similarly prepared QDs covered by GaAs.
State estimation for integrated vehicle dynamics control
This paper discusses a vehicle controller and a state estimator that was implemented and tested in a vehicle equipped with a combined braking and chassis control system to improve handling. The vehicle dynamics controller consists of a feed forward body roll compensation and a feedback stability controller. The estimator is based on a non-linear vehicle model combined with an extended Kalman filter. The Kalman filter is also connected to an algorithm, which estimates the tyre-road friction coefficient. The developed estimator was tested in a vehicle. Good results were achieved for normal and extreme driving manoeuvers.