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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.
How to do 3D quantum monte carlo in Silvaco Atlas
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I am trying to simulation LER, RDF, variation of Fin height and fin thickness of tri-gate FINFET. I have access to Silvaco atlas. Do anybody has experience on 3D monter carlo in Silvac Atlas. I...
Manual for the Generalized Bulk Monte Carlo Tool
24 Jun 2011 | Teaching Materials | Contributor(s): Raghuraj Hathwar, Dragica Vasileska
This manual describes the physics implemented behind the generalized bulk Monte Carlo tool.
Generalized Monte Carlo Presentation
20 Jun 2011 | Teaching Materials | 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,...
High Field Transport and the Monte Carlo Method for the Solution of the Boltzmann Transport Equation
23 Jul 2010 | Teaching Materials | 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.
Atomistic Simulations of Reliability
06 Jul 2010 | Teaching Materials | 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...
Bulk Monte Carlo: Implementation Details and Source Codes Download
01 Jun 2010 | Teaching Materials | 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...
Lecture 7: Initialization and Equilibrium
05 Jan 2010 | Presentation Materials | Contributor(s): Ashlie Martini
ECE 656 Lecture 31: Monte Carlo Simulation
01 Dec 2009 | Online Presentations | Contributor(s): Mark Lundstrom
Review of carrier scattering
Simulating carrier trajectories
Update after collision
Putting it all together
ECE 656 Lecture 30: Balance Equation Approach III
Carrier Temperature and Heat Flux
Balance equations in 3D
From Semi-Classical to Quantum Transport Modeling: Particle-Based Device Simulations
10 Aug 2009 | Teaching Materials | 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...
Band Structure Lab: First-Time User Guide
15 Jun 2009 | Teaching Materials | 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,...
Illinois PHYS 466, Lecture 18: Kinetic Monte Carlo (KMC)
04 May 2009 | Online Presentations | Contributor(s): David M. Ceperley, Omar N Sobh
Archimedes, GNU Monte Carlo simulator
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01 Apr 2009 | Tools | Contributor(s): Jean Michel D Sellier
GNU Monte Carlo simulation of 2D semiconductor devices, III-V materials
Quantum and Thermal Effects in Nanoscale Devices
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18 Sep 2008 | Online Presentations | 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...
Homework Assignment for Bulk Monte Carlo Lab: Velocity vs. Field for Arbitrary Crystallographic Orientations
22 Aug 2008 | Teaching Materials | 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.
Homework Assignment for Bulk Monte Carlo Lab: Arbitrary Crystallographic Direction
21 Aug 2008 | Teaching Materials | 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
Bulk Monte Carlo Code Described
02 Jul 2008 | Teaching Materials | 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...
Consistent Parameter Set for an Ensemble Monte Carlo Simulation of 4H-SiC
02 Jul 2008 | Papers | 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...
Computational Nanoscience, Lecture 20: Quantum Monte Carlo, part I
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20 May 2008 | Teaching Materials | 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...
Computational Nanoscience, Lecture 21: Quantum Monte Carlo, part II
20 May 2008 | Teaching Materials | Contributor(s): Jeffrey C Grossman, Elif Ertekin
This is our second lecture in a series on Quantum Monte Carlo methods. We describe the Diffusion Monte Carlo approach here, in which the approximation to the solution is not restricted by choice...