Computational study of an InGaN/GaN nanocolumn light-emitting diode

A comprehensive three-dimensional analysis of the operation of an In0.4Ga0.6N/GaN nanocolumn light-emitting diode is presented. Focus is put on the investigation of the nature and location of the emitting states. Calculations of strain and polarization-induced internal fields show that the strong lateral dependence of the potential gives rise to states confined to the periphery and to the center of the nanocolumn. However, lateral confinement of states near the column center is weak such that a quantum-well-like treatment of the remaining bound states seems appropriate where coherence is lost in the lateral directions. Within this picture, a coupled and self-consistent three-dimensional simulation of carrier transport and luminescence is presented, thus accounting for screening and lateral transport effects. Results are compared to a planar quantum-well device

tdkp/AQUA: Unified modeling of electroluminescence in nanostructures

ISSN:0306-8919ISSN:1572-817

Reliable k.p Band Structure Calculation for Nanostructures Using Finite Elements

ISSN:1569-8025ISSN:1572-813

Unified Simulation of Transport and Luminescence in Optoelectronic Nanostructures

ISSN:1569-8025ISSN:1572-813

Operator ordering, ellipticity and spurious solutions in k · p calculations of III-nitride nanostructures

ISSN:0306-8919ISSN:1572-817

Iterative solution of generalized eigenvalue problems from optoelectronics with trilinos

In this paper, we study the iterative solution of generalized Hermitian eigenvalue problems arising from the finite-element discretization of k·p models of optoelectronic nano systems. We are interested in computing the eigenvalues close to the band-gap which determine electronic and optical properties of a given system. Our work is based on the Trilinos project which provides an object-oriented software framework of integrated algorithms for the solution of large-scale physics problems. Trilinos enables users to combine state-of-the-art eigensolvers with efficient preconditioners, sparse solvers, and partitioning methods. Our study illustrates these possibilities and evaluates various algorithms for their suitability in the context of our physical problem setting