1. 재료현상을 관찰하는 또 하나의 방법 : 전산모사 2003 년 5 월 23 일 서울대 재료공학부 콜로퀴움 KIST 미래기술연구본부 이 광 렬

2. Today’s Talk What is atomic scale simulation? Role of atomic simulation in nano-materials research Brief survey of some cases Where should we go?

3. Computer Simulation 물리적으로 타당한 ( 혹은 타당하다고 생각되는 ) 단순계의 원리로부터 복잡계의 현상을 고찰하고자 하는 연구방법 16KeV Au 4 Cluster on Au (111)

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

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

6. Laplace’s 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.

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

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

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

11. KIST 1024 CPU Cluster System

12. GRID Environment

13. Moor’s 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

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

15. 과학에서 본다는 것의 의미 Telescope : Galilei (1610) – 새로운 우주관의 시발점 Microscope : Leeuwenhoek (1674) – 박테리아 정복의 시발점 신경세포의 가시화 : Golgi & Cajal (1906 Nobel Prize) –Neuroscience 의 시작 유적실험 : Millikan (1923 Nobel Prize) – 전하량 측정  근대적 원자구조의 이해 STM / AFM : Binnig & Rohrer (1986 Nobel Prize) –Nano-Technology 의 시작

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

17. Virtual Reality & Visualization

18. Nanomaterials

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

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

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

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

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

24. Nanoscience or Nanotechnology 물질을 원자 ∙ 분자 단위에서 규명하고 제어하여, 원자 ∙ 분자들을 적절히 분산 결합 시킴으로써 기존 물질의 변형 ∙ 개조 및 신물질의 창출이 가능한 기술 Needs Atomic Scale Understandings on the Structure, the Kinetics and the Properties Needs Atomic Scale Understandings on the Structure, the Kinetics and the Properties

25. Insufficient Experimental Tools

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

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

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

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!

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.

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

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

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

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

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.

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

37. Electron Emission from CNT 서울대학교, 고체물질이론 연구실

38. Array of sub-nano Ag Wire 포항공대 기능성물질연구센터 Self Assembling of CHQ Nanotube

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

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

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

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.

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)

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

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 ss ms ps min TIME DISTANCE 1A10A100A 1m1m 1mm Continuum Models - FEM/FDM - Monte Carlo Approach

46. First Principle Calculation Classical MD Continuum Simulation Multiscale Simulation

47. Multi-scale Approaches In Case of Fracture

48. Technologies Products 200020102020 National TRM for Modeling & Simulation Scale 별 전산모사 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 : 중점 전략 연구분야의 테크놀로지 로드맵 정립에 관한 연구 ( 기초기술연구회, 2002)

49. Multiscale Simulation Scale 별 전산모사 기술의 성숙 Ab-initio Calc. Classical MD Continuum Simul. Smart Inter-scale Interfacing Computing Method & Algorithm Massively Parallel Computing Facility Supercomputer & Code Optimization 다차원 전산모사의 성공요건

50. Experimental Research Groups Multiscale Interfacing Algorithm 개발 Application I/F Cluster Supercomputer & 최적 Computing 환경 Scale 별 전산모사 기술 Inter-Scale Interfacing 기술 수퍼컴 성능 최적화 및 병렬화 환경구축 First Principle Simulation Classical MD and MC Simulation Force Field DB Mesoscale and Continuum Simulation Device Simulation Multiscale Simulation 환경 Model

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.

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