155915-Thumbnail Image.png
Description
Semiconductor nanowires have the potential to emerge as the building blocks of next generation field-effect transistors, logic gates, solar cells and light emitting diodes. Use of Gallium Nitride (GaN) and other wide bandgap materials combines the advantages of III-nitrides along

Semiconductor nanowires have the potential to emerge as the building blocks of next generation field-effect transistors, logic gates, solar cells and light emitting diodes. Use of Gallium Nitride (GaN) and other wide bandgap materials combines the advantages of III-nitrides along with the enhanced mobility offered by 2-dimensional confinement present in nanowires. The focus of this thesis is on developing a low field mobility model for a GaN nanowire using Ensemble Monte Carlo (EMC) techniques. A 2D Schrödinger-Poisson solver and a one-dimensional Monte Carlo solver is developed for an Aluminum Gallium Nitride/Gallium Nitride Heterostructure nanowire. A GaN/AlN/AlGaN heterostructure device is designed which creates 2-dimensional potential well for electrons. The nanowire is treated as a quasi-1D system in this work. A self-consistent 2D Schrödinger-Poisson solver is designed which determines the subband energies and the corresponding wavefunctions of the confined system. Three scattering mechanisms: acoustic phonon scattering, polar optical phonon scattering and piezoelectric scattering are considered to account for the electron phonon interactions in the system. Overlap integrals and 1D scattering rate expressions are derived for all the mechanisms listed. A generic one-dimensional Monte Carlo solver is also developed. Steady state results from the 1D Monte Carlo solver are extracted to determine the low field mobility of the GaN nanowires.
Reuse Permissions


  • Download restricted.
    Download count: 4

    Details

    Title
    • Mobility Modeling of Gallium Nitride Nanowires
    Contributors
    Date Created
    2017
    Resource Type
  • Text
  • Collections this item is in
    Note
    • Masters Thesis Electrical Engineering 2017

    Machine-readable links