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Bulk GaAs Ensemble Monte Carlo

This resource has a 5.6 Ranking

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Usage Stats
Overall Period: Updated 20 Aug, 2008
Users: 34
Jobs: 89
Avg. exec. time: 13 secs
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Avg. Review: 0.0 out of 5 stars
Citations: 0

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Available Versions

  • 1.1 (published)
Version 1.1 - published on 26 Mar, 2008
Contributor(s) Mohamed Mohamed, Anjali Bharthuar, Umberto Ravaioli
University of Illinois, Urbana-Champaign
At a glance Basic Ensemble Monte Carlo code for the study of transport in bulk GaAs semiconductor.
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Description

This simple bulk Monte Carlo tool can be used to calculate observables of the electron ensemble in steady state or transient conditions. The calculation for the drift velocity as a function of electric field is readily computed. The algorithm implemented in the program is very general; nevertheless, the program uses parameters for gallium arsenide (GaAs).

The non-parabolic band structure approximation is used, with three valleys in the conduction band. The L and X satellite valleys are considered to have spherical constant energy surfaces, i.e. the effective mass used is the conductivity mass, taken as an average of longitudinal and transverse mass. The random flight selection is accomplished using an adaptation of the Constant Time Technique to calculate flight times.

References

[1] C. Jacoboni, L. Reggiani, “The Monte Carlo Method for Solution of Charge Transport in Semiconductor with Application to Covalent Materials ” Rev. Modern Physics, vol.55, 3, pp. 645-705, 1983.

[2] W Fawcett, AD Boardman, and S Swain, “Monte Carlo determination of electron transport properties in gallium arsenide”, J Phys Chem Solids, v 31, n 9, pp 1963-90, 1970

Cite this work

If you reference this work in a publication, please cite as follows:

  • Mohamed, Mohamed; Bharthuar, Anjali; Ravaioli, Umberto (2008), "Bulk GaAs Ensemble Monte Carlo," doi: 10254/nanohub-r2920.1.

    BibTex | EndNote

In addition, we would appreciate it if you would add the following acknowledgment to your publication:

  • Simulation services for results presented here were provided by the Network for Computational Nanotechnology (NCN) at nanoHUB.org

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