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GaInNAs: Fundamentals of a New Material System for Near-Infrared Optoelectronics

  • Michael Hetterich
Part of the Springer Series in Solid-State Sciences book series (SSSOL, volume 146)

Abstract

In recent years, dilute nitrides such as GaAsN and GaInNAs with typical nitrogen concentrations in the range of a few percent have become the subject of intense research, because they are promising materials for the realization of optoelectronic devices operating in the near infrared (NIR), including semiconductor lasers, resonant cavity enhanced photodetectors [1] and tandem solar cells [2]. Heterojunction bipolar transistors have also been investigated [3]. Of particular technological interest are quantum well structures based on the quaternary compound GaInNAs. As first suggested by Kondow et al. [4] they can form the active region of laser diodes operating at 1.3 or even 1.55 µm, the wavelengths used in present optical fibre communication networks. Up to now, InP-based InGaAsP devices are commonly applied for that purpose. However, this approach has several disadvantages [4, 5]: The electron confinement in InGaAsP structures is quite poor, leading to a high temperature sensitivity of the lasing threshold (i.e. a low T 0). As a practical consequence, thermoelectric cooling is often required. Furthermore, the development of InP-based vertical-cavity surface-emitting lasers (VCSELs) has turned out to be quite problematic, although the latter would have many advantages (e.g. single mode operation and improved coupling to optical fibres). This is due to the lack of materials lattice-matched to InP which comprise a sufficiently large refractive index contrast for the realization of high-reflectivity distributed Bragg reflectors (DBRs) and also have a sufficient thermal conductivity.

Keywords

Conduction Band Schrodinger Equation Single Mode Operation Heterojunction Bipolar Transistor High Temperature Sensitivity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag Berlin Heidelberg 2004

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  • Michael Hetterich

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