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## Boltzmann Transport Simulator for CNTs

Simulate Electron transport in Single-walled carbon nanotubes using an upwinding discretization of the Boltzmann transport equation in the relaxation time approximation.

Version **1.0.1a** - published on 25 Feb 2015

doi:10.4231/D3HX15R88 cite this

This tool is closed source.

### Reviews

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Anonymous@ on4.0 out of 5 stars

This nanohub program is available at DOI page. It is a very good tool to understand the basics of Monte Carlo simulation. It simulates carrier transport in a carbon nanotube with a constant electric field across its length (it does not have a gate) The author presented this tool in the class for this course and explained its working. The presentation for this tool is 1.

This tool can be used in three different modes:

The tool operates in two phases. The first phase is the setup phase where look-up-tables, dispersion, mesh, and electric field etc. are set up. The second phase is a Monte-Carlo simulation. Input

This page can take inputs on the applied bias (VDS), length of the nanotube (L), chiral vector (n,m) of the edge, temperature (T). The user can discretize in real space, k-space and time. Setup

The tool first begins by calculating the electronic band structure and phonon dispersion of the carbon nanotube and makes scattering tables before beginning a simulation. There is no need for any Poisson’s solver in this tool because there is no gate voltage. So a fixed field of E=\\frac} is assumed and all particles are accelerated by a constant electric field. Monte Carlo

A general outline of the Monte Carlo method shall follow in this paragraph. Individual carriers are simulated from the drain to the source keeping track of their momentum as is accelerated by the field. But with each time step the electric field in the channel due to a non-equilibrium distribution of the charges is updated – this is a minor correction to the electric field. Also, at each time step a random number is generated and based on the value of the random number a choice of a scattering event is made. The scattering probabilities are evaluated based on the scattering table that was calculated in the first step. The self-consistent solution emerges after several iterations of this solver. Feedback for the tool