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 (1-20 of 61)

  1. Anisotropic Schrödinger Equation Quantum Corrections for 3D Monte Carlo Simulations of Nanoscale Multigate Transistors

    05 Jan 2016 | | Contributor(s):: Karol Kalna, Muhammad Ali A. Elmessary, Daniel Nagy, Manuel Aldegunde

    IWCE 2015 presentation. We incorporated anisotropic 2D Schrodinger equation based quantum corrections (SEQC) that depends on valley orientation into a 3D Finite Element (FE) Monte Carlo (MC) simulation toolbox. The MC toolbox was tested against experimental ID-VG characteristics of the 22 nm...

  2. 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

  3. Atomistic Modeling: Past, Present, and Future, MGI, ICME, etc.

    03 Nov 2015 | | Contributor(s):: Paul Saxe

    I will present a perspective on atomistic modeling — tools using quantum methods such as DFT, as well as molecular dynamics and Monte Carlo methods based on forcefields — over the past 30 years or so. While we are all caught up in the present, it is important to remember and realize...

  4. Atomistic Simulations of Reliability

    01 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...

  5. 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...

  6. biomoca

    30 May 2006 | | Contributor(s):: Reza Toghraee, Umberto Ravaioli

    Ion channel simulator

  7. BioMOCA Suite

    04 Feb 2008 | | Contributor(s):: David Papke, Reza Toghraee, Umberto Ravaioli, Ankit Raj

    Simulates ion flow through a channel.

  8. 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...

  9. 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...

  10. Carbon Nanotube Electronics: Modeling, Physics, and Applications

    27 Jun 2013 | | Contributor(s):: Jing Guo

    In recent years, significant progress in understanding the physics of carbon nanotube electronic devices and in identifying potential applications has occurred. In a nanotube, low bias transport can be nearly ballistic across distances of several hundred nanometers. Deposition of high-k gate...

  11. Computational Nanoscience, Homework Assignment 4: Hard-Sphere Monte Carlo and Ising Model

    05 Mar 2008 | | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    In this assignment, you will explore the use of Monte Carlo techniques to look at (1) hard-sphere systems and (2) Ising model of the ferromagnetic-paramagnetic phase transition in two-dimensions. This assignment is to be completed following lecture 12 and using the "Hard Sphere Monte Carlo" and...

  12. Computational Nanoscience, Lecture 10: Brief Review, Kinetic Monte Carlo, and Random Numbers

    25 Feb 2008 | | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    We conclude our discussion of Monte Carlo methods with a brief review of the concepts covered in the three previous lectures. Then, the Kinetic Monte Carlo method is introduced, including discussions of Transition State Theory and basic KMC algorithms. A simulation of vacancy-mediated diffusion...

  13. 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,...

  14. Computational Nanoscience, Lecture 21: Quantum Monte Carlo, part II

    15 May 2008 | | 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 of a functional form for the wavefunction. The DMC approach is explained, and the fixed node...

  15. Computational Nanoscience, Lecture 27: Simulating Water and Examples in Computational Biology

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

    In this lecture, we describe the challenges in simulating water and introduce both explicit and implicit approaches. We also briefly describe protein structure, the Levinthal paradox, and simulations of proteins and protein structure using First Principles approaches and Monte Carlo...

  16. Computational Nanoscience, Lecture 4: Geometry Optimization and Seeing What You're Doing

    13 Feb 2008 | | Contributor(s):: Jeffrey C Grossman, Elif Ertekin

    In this lecture, we discuss various methods for finding the ground state structure of a given system by minimizing its energy. Derivative and non-derivative methods are discussed, as well as the importance of the starting guess and how to find or generate good initial structures. We also briefly...

  17. Computational Nanoscience, Lecture 7: Monte Carlo Simulation Part I

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

    The purpose of this lecture is to introduce Monte Carlo methods as a form of stochastic simulation. Some introductory examples of Monte Carlo methods are given, and a basic introduction to relevant concepts in statistical mechanics is presented. Students will be introduced to the Metropolis...

  18. Computational Nanoscience, Lecture 8: Monte Carlo Simulation Part II

    14 Feb 2008 | | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    In this lecture, we continue our discussion of Monte Carlo simulation. Examples from Hard Sphere Monte Carlo simulations based on the Metropolis algorithm and from Grand Canonical Monte Carlo simulations of fullerene growth on spherical surfaces are presented. A discussion of meaningful...

  19. Computational Nanoscience, Lecture 9: Hard-Sphere Monte Carlo In-Class Simulation

    19 Feb 2008 | | Contributor(s):: Elif Ertekin, Jeffrey C Grossman

    In this lecture we carry out simulations in-class, with guidance from the instructors. We use the HSMC tool (within the nanoHUB simulation toolkit for this course). The hard sphere system is one of the simplest systems which exhibits an order-disorder phase transition, which we will explore with...

  20. 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...