Controlled Kinetic Monte Carlo Simulation of Nanomanufacturing Processes

2011 ◽  
Author(s):  
Yan Wang
Keyword(s):  
Monte Carlo ◽  
Self Assembly ◽  
Kinetic Monte Carlo ◽  
Rare Event ◽  
Chemical Processes ◽  
Top Down ◽  
Efficient Tool ◽  
Bottom Up ◽  
Event Simulation ◽  
Physical And Chemical

Kinetic Monte Carlo (KMC) is regarded as an efficient tool for rare event simulation and has been used to simulate bottom-up self-assembly processes of nanomanufacturing. Yet, it cannot simulate top-down processes. In this paper, a new and generalized KMC mechanism, called controlled KMC or cKMC, is proposed to simulate complete physical and chemical processes. This generalization is enabled by the introduction of controlled events. In contrast to the traditional self-assembly events in KMC, controlled events occur at certain times, locations, or directions, which allows all events to be modeled. The applications of cKMC to several top-down and bottom-up processes are demonstrated.

Controlled Kinetic Monte Carlo Simulation for Computer-Aided Nanomanufacturing

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Yan Wang
Keyword(s):  
Monte Carlo ◽  
Self Assembly ◽  
Formal Model ◽  
Kinetic Monte Carlo ◽  
Rare Event ◽  
Top Down ◽  
Efficient Tool ◽  
Bottom Up ◽  
Event Simulation ◽  
Physical And Chemical

Kinetic Monte Carlo (KMC) is regarded as an efficient tool for rare event simulation and has been applied in simulating bottom–up self-assembly processes of nanomanufacturing. Yet it has limitations to simulate top–down processes. In this paper, a new and generalized KMC mechanism, called controlled KMC or controlled KMC (cKMC), is proposed to simulate complete physical and chemical processes. This generalization is enabled by the introduction of controlled events. In contrast to the traditional self-assembly events in KMC, controlled events occur at certain times, locations, or directions, which allows all events to be modeled. A formal model of cKMC is also presented to show the generalization. The applications of cKMC to several top–down and bottom–up processes are demonstrated.

scholarly journals Multiscale additive manufacturing of polymers using 3D photo-printable self-assembling ionic liquid monomers

2019 ◽  
Vol 4 (3) ◽  
pp. 580-585 ◽  
Author(s):  
Bineh G. Ndefru ◽  
Bryan S. Ringstrand ◽  
Sokhna I.-Y. Diouf ◽  
Sönke Seifert ◽  
Juan H. Leal ◽  
...  
Keyword(s):  
Ionic Liquid ◽  
Additive Manufacturing ◽  
Self Assembly ◽  
Low Cost ◽  
Low Resolution ◽  
Top Down ◽  
Bottom Up ◽  
Cost Approach ◽  
Self Assembling ◽  
Nanoscale Features

Combining bottom-up self-assembly with top-down 3D photoprinting affords a low cost approach for the introduction of nanoscale features into a build with low resolution features.

Kinetic Monte Carlo Simulations of Strain-Induced Nanopatterning on Hexagonal Surfaces

2002 ◽  
Vol 731 ◽  
Author(s):  
M.I. Larsson ◽  
B. Lee ◽  
R. Sabiryanov ◽  
K. Cho ◽  
W. Nix ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Saddle Point ◽  
Self Assembly ◽  
Nearest Neighbor ◽  
Kinetic Monte Carlo ◽  
Research Field ◽  
Surface Strain ◽  
Manufacturing Cost ◽  
Component Size ◽  
Quantum Mechanical Effects

AbstractGuided self assembly of periodic arrays of quantum dots has recently emerged as an important research field not only to reduce component size and manufacturing cost but also to explore and apply quantum mechanical effects in novel nanodevices. The intention of this kinetic Monte Carlo (KMC) simulation study is to investigate self-organized nanopatterning on hexagonal surfaces for relaxed periodic surface strain fields applied to Pt(111) epitaxy. The KMC model is a full diffusion bond-counting model including nearest neighbor as well as second-nearest neighbor interactions with an event catalogue consisting of 8989 events modeling the effect of the biaxial surface strain field. The strain dependence of the fcc site and the saddle point for a Pt adatom migrating on top of the Pt(111) surface is calculated using the embedded atom method. Both the valley and the saddle point energies show an excellent linear dependence on the strain. These results are applied in the KMC model. The surface strain in this study is caused by a hexagonal network of dislocations at the interface between the substrate and a mismatched epitaxial layer. How the selforganization of deposited atoms is influenced by the surface strain will be addressed.

Block Copolymer Films Hierarchical Assembly in Confinement

2007 ◽  
Vol 364-366 ◽  
pp. 437-441
Author(s):  
Yong Zhi Cao ◽  
Shen Dong ◽  
Ying Chun Liang ◽  
Tao Sun ◽  
Yong Da Yan
Keyword(s):  
Block Copolymer ◽  
Self Assembly ◽  
Structural Control ◽  
Computer Control ◽  
The Self ◽  
Top Down ◽  
Bottom Up ◽  
Dimensional Precision ◽  
Periodic Arrays ◽  
Copolymer Films

Ultrathin block copolymer films are promising candidates for bottom-up nanotemplates in hybrid organic-inorganic electronic, optical, and magnetic devices. Key to many future applications is the long range ordering and precise placement of the phase-separated nanoscale domains. In this paper, a combined top-down/bottom-up hierarchical approach is presented on how to fabricate massive arrays of aligned nanoscale domains by means of the self-assembly of asymmetric poly (styrene-block-ethylene/butylenes-block-styrene) (SEBS) tirblock copolymers in confinement. The periodic arrays of the poly domains were orientated via the introduction of AFM micromachining technique as a tool for locally controlling the self-assembly process of triblock copolymers by the topography of the silicon nitride substrate. Using the controlled movement of 2- dimensional precision stage and the micro pressure force between the tip and the surface by computer control system, an artificial topographic pattern on the substrate can be fabricated precisely. Coupled with solvent annealing technique to direct the assembly of block copolymer, this method provides new routes for fabricating ordered nanostructure. This graphoepitaxial methodology can be exploited in hybrid hard/soft condensed matter systems for a variety of applications. Moreover, Pairing top-down and bottom-up techniques is a promising, and perhaps necessary, bridge between the parallel self-assembly of molecules and the structural control of current technology.

scholarly journals Monte Carlo and Kinetic Monte Carlo Models for Deposition Processes: A Review of Recent Works

2021 ◽  
Vol 9 ◽  
Author(s):  
Nikolaos Cheimarios ◽  
Deifilia To ◽  
George Kokkoris ◽  
George Memos ◽  
Andreas G. Boudouvis
Keyword(s):  
Monte Carlo ◽  
Vapor Deposition ◽  
Kinetic Monte Carlo ◽  
Spatial Scales ◽  
Chemical Vapor ◽  
Reaction Diffusion ◽  
Atomic Layer ◽  
Surface Phenomena ◽  
Deposition Processes ◽  
Physical And Chemical

Monte Carlo (MC) and kinetic Monte Carlo (kMC) models are widely used for studying the physicochemical surface phenomena encountered in most deposition processes. This spans from physical and chemical vapor deposition to atomic layer and electrochemical deposition. MC and kMC, in comparison to popular molecular methods, such as Molecular Mechanics/Dynamics, have the ability to address much larger time and spatial scales. They also offer a far more detailed approach of the surface processes than continuum-type models, such as the reaction-diffusion models. This work presents a review of the modern applications of MC/kMC models employed in deposition processes.

scholarly journals Improved algorithms for rare event simulation with heavy tails

2006 ◽  
Vol 38 (2) ◽  
pp. 545-558 ◽  
Author(s):  
Søren Asmussen ◽  
Dirk P. Kroese
Keyword(s):  
Monte Carlo ◽  
Relative Error ◽  
Heavy Tails ◽  
Numerical Study ◽  
Rare Event ◽  
Random Variable ◽  
Event Simulation ◽  
Conditional Monte Carlo ◽  
Heavy Tailed ◽  
Geometric Random Variable

The estimation of P(Sn>u) by simulation, where Sn is the sum of independent, identically distributed random varibles Y1,…,Yn, is of importance in many applications. We propose two simulation estimators based upon the identity P(Sn>u)=nP(Sn>u, Mn=Yn), where Mn=max(Y1,…,Yn). One estimator uses importance sampling (for Yn only), and the other uses conditional Monte Carlo conditioning upon Y1,…,Yn−1. Properties of the relative error of the estimators are derived and a numerical study given in terms of the M/G/1 queue in which n is replaced by an independent geometric random variable N. The conclusion is that the new estimators compare extremely favorably with previous ones. In particular, the conditional Monte Carlo estimator is the first heavy-tailed example of an estimator with bounded relative error. Further improvements are obtained in the random-N case, by incorporating control variates and stratification techniques into the new estimation procedures.

Deriving molecular bonding from a macromolecular self-assembly using kinetic Monte Carlo simulations

2008 ◽  
Vol 77 (20) ◽  
Author(s):  
Fabien Silly ◽  
Ulrich K. Weber ◽  
Adam Q. Shaw ◽  
Victor M. Burlakov ◽  
Martin R. Castell ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Self Assembly ◽  
Kinetic Monte Carlo ◽  
Kinetic Monte Carlo Simulations ◽  
Molecular Bonding

scholarly journals Improved algorithms for rare event simulation with heavy tails

2006 ◽  
Vol 38 (02) ◽  
pp. 545-558 ◽  
Author(s):  
Søren Asmussen ◽  
Dirk P. Kroese
Keyword(s):  
Monte Carlo ◽  
Relative Error ◽  
Heavy Tails ◽  
Numerical Study ◽  
Rare Event ◽  
Random Variable ◽  
Event Simulation ◽  
Conditional Monte Carlo ◽  
Heavy Tailed ◽  
Geometric Random Variable

The estimation of P(S n >u) by simulation, where S n is the sum of independent, identically distributed random varibles Y 1 ,…,Y n , is of importance in many applications. We propose two simulation estimators based upon the identity P(S n >u)=nP(S n >u, M n =Y n ), where M n =max(Y 1 ,…,Y n ). One estimator uses importance sampling (for Y n only), and the other uses conditional Monte Carlo conditioning upon Y 1 ,…,Y n−1. Properties of the relative error of the estimators are derived and a numerical study given in terms of the M/G/1 queue in which n is replaced by an independent geometric random variable N. The conclusion is that the new estimators compare extremely favorably with previous ones. In particular, the conditional Monte Carlo estimator is the first heavy-tailed example of an estimator with bounded relative error. Further improvements are obtained in the random-N case, by incorporating control variates and stratification techniques into the new estimation procedures.

Sign in / Sign up

Export Citation Format

Share Document