Molecular Dynamics Simulations of Thin Film Growth · 2008-08-07 · Molecular Dynamics Simulations...
Transcript of Molecular Dynamics Simulations of Thin Film Growth · 2008-08-07 · Molecular Dynamics Simulations...
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Daniel Bowring3rd International Workshop on Thin Films
July 22, 2008
Molecular Dynamics Simulations of Thin
Film Growth
1Tuesday, July 22, 2008
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Overview
• Why simulate film growth?
• Overview of MD
• Features of this model
• Future work
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Motivation
• Grain boundaries have an effect on surface resistance.
• Film quality is dependent on deposition technique:
• surface roughness
• defect density
• Backsputtering from energetic condensation
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Motivation (cont’d.)
Parameter space for film deposition is large:
• deposition energy
• substrate temperature
• substrate structure (amorphous, polycrystalline, single-crystal)
• bias voltage effects
These properties can all be simulated!
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Molecular Dynamics
• Integrate Newton’s laws of motion for many particles, watch system evolve.
• F2 are two-body forces, F3 are three-body. Many-body interactions also possible (jellium, EAM, etc.).
mid!vi
dt=
!
j
F2 (!ri,!rj) +!
j
!
k
F3 (!ri,!rj ,!rk) + · · ·
d!ri
dt= !vi
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Molecular Dynamics (cont’d.)
• Computationally intensive:
• For single processors, MD viable for N~104, total time ~ ns.
• Integrating Newton’s laws takes 2-3% of simulation time.
• Atom-by-atom interatomic potentials are the real computational burden.
6Tuesday, July 22, 2008
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LAMMPS
• Open-source MD code developed by S.J. Plimpton at Sandia National Labs
• LAMMPS = Large-scale Atomic/Molecular Massively Parallel Simulator
S.J. Plimpton. Fast Parallel Algorithms for Short-Range Molecular Dynamics.J. Comp. Phys. 117 (1995) 1-19.
http://lammps.sandia.gov/index.html
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Interatomic Potential
• This analysis uses the embedded atom method (EAM) potential:
Fi = embedding energy
Φ = short-range pair potential
ρ = host density at i
Rij = distance between atoms i,j.
Etot =!
i
Fi (!h,i) +12
!
i !=j
"ij (Rij)
Pair interaction
Implantation energy
M.S. Daw, M.I. Baskes. Phys. Rev. B. 29 (1984) 12.
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Interatomic Potential (cont’d.)
• To our knowledge, no EAM potential has been developed yet that agrees with empirically-observed properties of Nb.
• The complicated electronic structure of transition metals makes an analytic determination of such a potential non-trivial.
• More on this shortly.
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Simulations in Development
• surface self-diffusion
• competitive grain growth
• defect density
• backsputtering
• preferential sputtering rates
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Some Preliminary Work
• LAMMPS-based simulation of Cu self-diffusion on a substrate with (110) and (111) grains.
• Substrate held at some constant temperature, consistent with the canonical ensemble.
• Movies produced using AtomEye, an atomistic configuration viewer developed by Ju Li at U. Penn.J. Li. Modeling Simul. Mater. Sci. Eng. 11 (2003) 173.
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Self Diffusion: Cu at 50 K
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Self Diffusion: Cu Atoms at 40 K
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Why Copper?
• These movies illustrate a proof-of-concept model.
• Cu very well understood.
• Modeling transition elements (like Nb) is non-trivial due to resonances in the electron density.
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Role of Electron Density
• How to approach an N-body quantum-mechanical problem?
• QM wavefunction has 4N degrees of freedom (position + spin).
• W. Kohn et al. mapped ground-state w.f. to the electron density:
• All material properties (e.g. specific heat) obtained from electron density!
c.f. W. Kohn, L.J. Sham. Phys. Rev. 140 (1965) 1133-1138.
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Role of Electron Density (cont’d.)
s-band (simple metals)superimposed d-band (transition metals)
dens
ity o
f sta
tes
energy
• Uniform density approximations (e.g. jellium) suitable for simple metals.
• Transition metals are complicated: d-band resonance invalidates many LDAs.
• Analytic approaches are not generally applied to the electron density of bcc transition metals.
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Nb interatomic potential from force-matching method
Implementation of parallel computing resources at JLAB
Future Work
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Ab Initio Force-Matching Method
• Possible to develop an EAM potential for Nb using empirical data.
• c.f. F. Ercolessi, J.B. Adams. Europhys. Lett. 26 (1994) 8.
• Similar to the least-squares kinematic fit procedure used in high-energy physics.
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Force-Matching Method (cont’d.)
• Electron density ρ(r) is approximated as a set of points in α-dimensional parameter space.
• Objective function Z(α) characterizes the mean square fitting error in ρ(r), using an ab initio force database as reference.
• Z(α) is minimized with respect to constraints on parameter space (e.g. stacking fault energy, elastic constants)
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Parallel Processing
• Practically, simulations on a single desktop are limited to:
- 104 atoms
- one nanosecond
• Complex (i.e. interesting) simulations require parallel computing platforms.
• This analysis can be easily ported to JLAB’s parallel computing farm via MPI.
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Acknowledgements
• Larry Phillips
• Xin Zhao
• David Srolovitz
• Jerry Floro
• Stephen Plimpton
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