Slide 1

: 2003 5 23 KIST

Slide 2

Todays Talk What is atomic scale simulation? Role of atomic simulation in nano-materials research Brief survey of some cases Where should we go?

Slide 3

Computer Simulation ( ) 16KeV Au 4 Cluster on Au (111)

Slide 4

Time Evolution of R i and v i Molecular Dynamic Simulation i Empirical Approach First Principle Approach Interatomic Potentials

Slide 5

R. Feynman, Lectures on Physics, Ch. 7 & 9 (1963) Theory and Observations (Newtonian Mechanics) Motion of a Mass on a Spring Orbit of Sirius Double Star

Slide 6

Laplaces Dream (1814) Pierre-Simon Laplace (1749-1827) Given for one instant, an intelligence which could comprehend all the forces by which nature is animated and the respective situation of the beings , nothing would be uncertain and the future, as the past, would be present to its eyes.

Slide 7

Slide 8

The intelligence in 21 st Century High computing power at low cost High performance visualization tools

Slide 9

New Era of Computer Simulation C-plant @ Sandia National Lab. Beowulf Cluster @ CALTECH Alpha Cluster @ SAITAvalon @ Los Alamos National Lab.

Slide 10

80 Execution Nodes X2 Pentium III (850~2050MHz) connected by 100Mbps Ethernet and Myrinet 66 Gbyte RAM 4.9 Terabyte HDD 2 Head Execution Nodes X4 Pentium III Xeon (700,2000MHz) for Head Execution 4Gbyte RAM 3,280Gbyte HDD 100Gflops KIST Beowulf System

Slide 11

KIST 1024 CPU Cluster System

Slide 12

GRID Environment

Slide 13

Moors Law in Atomic Simulation Empirical MD Number of atoms has doubled every 19 months. 864 atoms in 1964 (A. Rahman) 6.44 billion atoms in 2000 First Principle MD Number of atoms has doubled every 12 months. 8 atoms in 1985 (R. Car & M. Parrinello) 111,000 atoms in 2000

Slide 14

The intelligence in 21 st Century High computing power at low cost High performance visualization tools

Slide 15

Telescope : Galilei (1610) Microscope : Leeuwenhoek (1674) : Golgi & Cajal (1906 Nobel Prize) Neuroscience : Millikan (1923 Nobel Prize) STM / AFM : Binnig & Rohrer (1986 Nobel Prize) Nano-Technology

Slide 16

Min Max 4 3 2 1 0 5 In case of 75 eV

Slide 17

Virtual Reality & Visualization

Slide 18

Nanomaterials

Slide 19

~ nm Characteristics of Nanotechnology Continuum media hypothesis is not allowed. Diffusion & Mechanics Band Theory

Slide 20

Case I : Size Dependent Properties Atomic Orbitals N=1 Molecules N=2 Clusters N=10 Q-Size Particles N=2,000 Semiconductor N>>2,000 h Energy h Conduction Band Valence Band Vacuum CdSe Nanoparticles Smaller Size

Slide 21

Case II : Scale Down Issues 2~4nm 0.13 m 10 nm Kinetics based on continuum media hypothesis is not sufficient.

Slide 22

Chracteristics of Nanotechnology Continuum media hypothesis is not allowed. Large fraction of the atom lies at the surface or interface. Abnormal Wetting Abnormal Melting of Nano Particles Chemical Instabilities

Slide 23

Case IV : GMR Spin Valve Major Materials Issue is the interfacial structure and chemical diffusion in atomic scale Major Materials Issue is the interfacial structure and chemical diffusion in atomic scale

Slide 24

Nanoscience or Nanotechnology , Needs Atomic Scale Understandings on the Structure, the Kinetics and the Properties Needs Atomic Scale Understandings on the Structure, the Kinetics and the Properties

Slide 25

Insufficient Experimental Tools

Slide 26

Methodology of Science & Technology Synthesis & Manipulation Analysis & Characterization Analysis & Characterization Modeling & Simulation Modeling & Simulation

Slide 27

Methodology of Nanotechnology Synthesis & Manipulation Modeling & Simulation Modeling & Simulation Analysis & Characterization Analysis & Characterization

Slide 28

Atomic Scale Simulation of Interfacial Intermixing during Low Temperature Deposition in Co-Al System

Slide 29

Magnetic RAM (MRAM) 1 nm Properties of MRAM are largely depends on the Interface Structures of Metal/Metal or Metal/Insulator Controlling & Understanding The atomic behavior at the interface are fundamental to improve the performance of the nano-devices!

Slide 30

Conventional Thin Film Growth Model Conventional thin film growth model simply assumes that intermixing between the adatom and the substrate is negligible. Conventional thin film growth model simply assumes that intermixing between the adatom and the substrate is negligible.

Slide 31

Adatom (0.1eV, normal incident) Substrate Program : XMD 2.5.30 x,y-axis : Periodic Boundary Condition z-axis : Open Surface dt : 0.5fs, calculation time : 5ps/atom [100] [001] [010] z y x 300K Initial Temperature 300K Constant Temperature Fix Position

Slide 32

Depostion Behavior on (001) Reaction Coordinate Co on Al (001)

Slide 33

Deposition Behavior on (001) Al on Co (001)

Slide 34

Deposition Behavior on (001) Al on Al (100) Al on Al (001)

Slide 35

Thin Film Growth Conventional thin film growth model assumes negligible intermixing between the adatom and the substrate atom. In nano-scale processes, the model need to be extended to consider the atomic intermixing at the interface. Conventional Thin Film Growth Model Calculations of the acceleration of adatom and the activation barrier for the intermixing can provide a criteria for the atomic intermixing.

Slide 36

ABDC {111} plane Tensile Test of Cu Nanowires Computational Semiconductor Technology Lab.

Slide 37

Electron Emission from CNT ,

Slide 38

Array of sub-nano Ag Wire Self Assembling of CHQ Nanotube

Slide 39

Search for New DMS Materials SiC:TM or AlN:TM DOS of AlN

Slide 40

Search for New DMS Materials SiC:TM or AlN:TM DOS of AlN Half Metal!!

Slide 41

Spin as new degree of freedom in quantum device structures Combine nonvolatile character with band gap engineering New Functionality Motivation spin-LED FM p+ ~ ~ ~ ~ ~ ~ circularly polarized output 2DEG transport 2DEG V g spin-FET source gate drain single transistor nonvolatile memory Spintronics

Slide 42

Role of Computational Modeling Provide physical intuition and insight where the continuum world is replaced by the granularity of the atomic world. Bridge the Gap between Fundamental Materials Science and Materials Engineering Provide virtual experimental tools where the physical experiment or analysis fails. Allow fundamental theory (i.e.quantum mechanics) to be applied to a complex problem.

Slide 43

Importance of Modeling & Simulation The emergence of new behaviors and processes in nanostructures, nanodevices and nanosystems creates an urgent need for theory, modeling, large-scale computer simulation and design tools and infrastructure in order to understand, control and accelerate the development in new nano scale regimes and systems. NSF announcement for multi-scale, multi-phenomena theory, modeling and simulation at nanoscle activity (2000)

Slide 44

Materials Science in 21 st Century Computational simulation was frequently emphasized in many articles. H. Gleiter : Nanostructured Materials W.J. Boettinger et al : Solidification Microstructures J. Hafner : Atomic-scale Computational Materials Science A. Needleman : Computational Mechanics in mesoscale

Slide 45

Hierarchy of Computer Simulation Fundamental Models - Ab initio MD - First Principle Calculation Atomic Level Simulation - Monte Carlo Approach - Classical MD Engineering Design ns fs ss ms ps min TIME DISTANCE 1A10A100A 1m1m 1mm Continuum Models - FEM/FDM - Monte Carlo Approach

Slide 46

First Principle Calculation Classical MD Continuum Simulation Multiscale Simulation

Slide 47

Multi-scale Approaches In Case of Fracture

Slide 48

Technologies Products 200020102020 National TRM for Modeling & Simulation Scale Molecular Manipulation Smart Nanosystem & Process Designer Multiscale Materials Simulation Empirical MD Quantum MD Mesoscale Simul. Virtual Reality & Smart MMII High Performance Computing & Algorithm Cluster Computing Smart Parallel Algorithm Quantum Computing Integrated Simulation Technology Multiscale Simulator Nano Materials & System DB Source : ( , 2002)

Slide 49

Multiscale Simulation Scale Ab-initio Calc. Classical MD Continuum Simul. Smart Inter-scale Interfacing Computing Method & Algorithm Massively Parallel Computing Facility Supercomputer & Code Optimization

Slide 50

Experimental Research Groups Multiscale Interfacing Algorithm Application I/F Cluster Supercomputer & Computing Scale Inter-Scale Interfacing First Principle Simulation Classical MD and MC Simulation Force Field DB Mesoscale and Continuum Simulation Device Simulation Multiscale Simulation Model

Slide 51

Within five to ten years, there must be robust tools for quantitative understanding of structure and dynamics at the nanoscale, without which the scientific community will have missed many scientific opportunities as well as a broad range of nanotechnology applications.

Slide 52

http://diamond.kist.re.kr/SMS