Tags: Monte Carlo

Description

Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in simulating physical and mathematical systems. Because of their reliance on repeated computation of random or pseudo-random numbers, these methods are most suited to calculation by a computer and tend to be used when it is unfeasible or impossible to compute an exact result with a deterministic algorithm.

Learn more about quantum dots from the many resources on this site, listed below. More information on Monte Carlo method can be found here.

Resources (21-40 of 61)

  1. Test for Monte Carlo Learning Module

    30 Jul 2011 | Contributor(s):: Dragica Vasileska, Gerhard Klimeck

    this is a test for the MC Learning Module.

  2. Single Particle and Ensemble Monte Carlo Method

    30 Jun 2011 | | Contributor(s):: Dragica Vasileska

    This set of handwritten notes is part of the Semiconductor Transport class.

  3. Manual for the Generalized Bulk Monte Carlo Tool

    24 Jun 2011 | | Contributor(s):: Raghuraj Hathwar, Dragica Vasileska

    This manual describes the physics implemented behind the generalized bulk Monte Carlo tool.

  4. Generalized Monte Carlo Presentation

    20 Jun 2011 | | Contributor(s):: Dragica Vasileska

    This presentation goes along with the Bulk Monte Carlo tool on the nanoHUB that calculates transients and steady-state velocity-field characteristics of arbitrary materials such as Si, Ge, GaAs, GaN, SiC, etc. The tool employs a non-parabolic bandstructure.

  5. High Field Transport and the Monte Carlo Method for the Solution of the Boltzmann Transport Equation

    23 Jul 2010 | | Contributor(s):: Dragica Vasileska

    This set of slides first describes the path-integral solution of the BTE and then discusses in details the Monte Carlo Method for the Solution of the Boltzmann Transport Equation.

  6. Atomistic Simulations of Reliability

    06 Jul 2010 | | Contributor(s):: Dragica Vasileska

    Discrete impurity effects in terms of their statistical variations in number and position in the inversion and depletion region of a MOSFET, as the gate length is aggressively scaled, have recently been researched as a major cause of reliability degradation observed in intra-die and die-to-die...

  7. Bulk Monte Carlo: Implementation Details and Source Codes Download

    01 Jun 2010 | | Contributor(s):: Dragica Vasileska, Stephen M. Goodnick

    The Ensemble Monte Carlo technique has been used now for over 30 years as a numerical method to simulate nonequilibrium transport in semiconductor materials and devices, and has been the subject of numerous books and reviews. In application to transport problems, a random walk is generated to...

  8. Lecture 7: Initialization and Equilibrium

    05 Jan 2010 | | Contributor(s):: Ashlie Martini

    Topics:Initial positionsInitial velocitiesEvaluating equilibrium

  9. ECE 656 Lecture 31: Monte Carlo Simulation

    01 Dec 2009 | | Contributor(s):: Mark Lundstrom

    Outline:IntroductionReview of carrier scatteringSimulating carrier trajectoriesFree flightCollisionUpdate after collisionPutting it all togetherSummary

  10. ECE 656 Lecture 30: Balance Equation Approach III

    01 Dec 2009 | | Contributor(s):: Mark Lundstrom

    OutlineCarrier Temperature and Heat FluxBalance equations in 3DHeterostructuresSummary

  11. From Semi-Classical to Quantum Transport Modeling: Particle-Based Device Simulations

    10 Aug 2009 | | Contributor(s):: Dragica Vasileska

    This set of powerpoint slides series provides insight on what are the tools available for modeling devices that behave either classically or quantum-mechanically. An in-depth description is provided to the approaches with emphasis on the advantages and disadvantages of each approach. Conclusions...

  12. Band Structure Lab: First-Time User Guide

    15 Jun 2009 | | Contributor(s):: Abhijeet Paul, Benjamin P Haley, Gerhard Klimeck

    This document provides useful information about Band Structure Lab. First-time users will find basic ideas about the physics behind the tool such as band formation, the Hamiltonian description, and other aspects. Additionally, we provide explanations of the input settings and the results of the...

  13. Illinois PHYS 466, Lecture 18: Kinetic Monte Carlo (KMC)

    04 May 2009 | | Contributor(s):: David M. Ceperley, Omar N Sobh

  14. Archimedes, GNU Monte Carlo simulator

    29 May 2008 | | Contributor(s):: Jean Michel D Sellier

    GNU Monte Carlo simulation of 2D semiconductor devices, III-V materials

  15. Quantum and Thermal Effects in Nanoscale Devices

    18 Sep 2008 | | Contributor(s):: Dragica Vasileska

    To investigate lattice heating within a Monte Carlo device simulation framework, we simultaneously solve the Boltzmann transport equation for the electrons, the 2D Poisson equation to get the self-consistent fields and the hydrodynamic equations for acoustic and optical phonons. The phonon...

  16. Homework Assignment for Bulk Monte Carlo Lab: Velocity vs. Field for Arbitrary Crystallographic Orientations

    21 Aug 2008 | | Contributor(s):: Dragica Vasileska, Gerhard Klimeck

    User needs to calculate and compare to experiment the velocity field characteristics for electrons in Si for different crystalographic directions and 77K and 300K temperatures.

  17. Homework Assignment for Bulk Monte Carlo Lab: Arbitrary Crystallographic Direction

    20 Aug 2008 | | Contributor(s):: Dragica Vasileska, Gerhard Klimeck

    This exercise teaches the users how the average carrier velocity, average carrier energy and vally occupation change with the application of the electric field in arbitrary crystalographic direction

  18. Bulk Monte Carlo Code Described

    01 Jul 2008 | | Contributor(s):: Dragica Vasileska

    In this tutorial we give implementation details for the bulk Monte Carlo code for calculating the electron drift velocity, velocity-field characteristics and average carrier energy in bulk GaAs materials. Identical concepts with minor details apply to the development of a bulk Monte Carlo code...

  19. Consistent Parameter Set for an Ensemble Monte Carlo Simulation of 4H-SiC

    01 Jul 2008 | | Contributor(s):: Dragica Vasileska

    A consistent parameter set is presented for Ensemble Monte Carlo simulation that simultaneously reproduces the experimental low-field and high-field characteristic transport parameters of 4H SiC.D. Vasileska and S. M. Goodnick, Computational Electronics, Morgan and Claypool, 2006.Freescale...

  20. Computational Nanoscience, Lecture 20: Quantum Monte Carlo, part I

    15 May 2008 | | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    This lecture provides and introduction to Quantum Monte Carlo methods. We review the concept of electron correlation and introduce Variational Monte Carlo methods as an approach to going beyond the mean field approximation. We describe briefly the Slater-Jastrow expansion of the wavefunction,...