september 25, 2007ieee nano-net, catania, italy1 networking behavior in thin film and nanostructure...

September 25, 2007 IEEE Nano-Net, Catania, Italy 1

Networking Behavior in Thin Film and

Nanostructure Growth Dynamics

Tansel Karabacak

U of Arkansas – Little Rock

Applied Science Department

[email protected]

Murat Yuksel

U of Nevada – Reno

CSE [email protected]

Hasan Guclu

Los Alamos National Lab

Complex Systems Group

[email protected]


Talk Outline

Motivation: Dynamic Effects in Growth

A Network Modeling Approach Initial Results Future Work


Motivation: Deposition Techniques

definition

plasma

Sputter deposition

sin/cos~

Precursor gas

Chemical vapor deposition (CVD)/plasma enhanced CVD

cos~o70

Oblique angle deposition

Deposition angle (

Flux distributions:

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

0.0

0.2

0.4

0.6

0.8

1.0

Nor

mal

ized

flux

dis

trib

utio

n F

(

CVD Obliqueangle dep

Sputter dep

Thermal evap

Thermal evaporation(not including MBE)

0

definition

definition

plasma

Sputter deposition

sin/cos~

plasmaplasma

Sputter deposition

sin/cos~

Precursor gas


cos~

Precursor gas


cos~o70

Oblique angle deposition

Deposition angle (

Flux distributions:

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

0.0

0.2

0.4

0.6

0.8

1.0

Nor

mal

ized

flux

dis

trib

utio

n F

(


Sputter dep

Thermal evap

Deposition angle (

Flux distributions:

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

0.0

0.2

0.4

0.6

0.8

1.0

Nor

mal

ized

flux

dis

trib

utio

n F

(


Sputter dep

Thermal evap

Thermal evaporation(not including MBE)

0

Thermal evaporation(not including MBE)Thermal evaporation(not including MBE)

0



A typical thin film and nanostructure growth involves four main dynamic effects: shadowing surface diffusion reemission noise

Characterizing these effects is vital to understand the shapes of nanostructures



theory

theory(shadowing)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ObliqueCVDSputteringEvaporation

Gro

wth

exp

onen

t,

Deposition method

(smoothing)theory

theory(shadowing)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

ObliqueCVDSputteringEvaporation

Gro

wth

exp

onen

t,

Deposition method

(smoothing)

Theoretical models (e.g., dynamic

scaling) have not been able explain growth behavior

well..

root-mean-square roughness = time



Monte Carlo simulations have been able to explain growth behavior well..

2

3

1

4

x

y

z

2

3

1

4

x

y

z

Fundamental Questions:

Can we explain the growth behavior in simple theoretical terms? Are there any universal behaviors in the growth

process?


A Network Modeling Approach…

Key Idea: Use the simulations data to map the growth process to a corresponding network model.

For now, we focus on the reemission and shadowing effects, as they are the dominant ones.

AB

Non-sticking & “re-emission”

Sticking & deposition

Incident particles(atoms/molecules)

Particles being captured mostly by the hills due to the “shadowing” effect

AB

Non-sticking & “re-emission”

Sticking & deposition

Incident particles(atoms/molecules)

Particles being captured mostly by the hills due to the “shadowing” effect

A reemitting particle means that there is a “relationship” between the starting and ending points of the reemission.



What is a “node”? grid-based

raw grid points

cluster-based mainly models shadowing effects defined based on the current surface morphology

granularity matters..

hill

valley



What is a “link”? mainly models the reemission effect unidirectional vs. bidirectional

again, granularity matters.. possible to classify the nodes:

source (hill), sink (valley), routers



Several more abstractions are possible link capacity: maximum # of particles that can

physically reemit from point A to point B on the surface

link propagation delay: physical distance between point A to point B

traffic: particles/time bits/time

The challenge is to illustrate physical meanings to these abstractions..


Initial Results Looked at thin film growth simulations

Chemical vapor deposition (CVD) surface size: 512 x 512 lattice units two different sticking coefficients: s=0.1, s=0.9 simulated reemission, shadowing, and noise effects

Network model assumed each lattice unit on the 512 x 512 grid is

potentially a node took four snapshots at different surface thicknesses

during the simulation each snapshot is the network corresponding to the

trajectories of 10 x 512 x 512 particles (adatoms)


Initial Results

Grown surfaces at different thicknesses and their corresponding network models

s=0.9


Initial Results

Degree distributions: mainly Exponential, becomes

power-law-like as time goes by

Degree distribution converges to the same for both sticking coefficients!

Degree distribution converges to the same for both sticking coefficients!


Initial Results

Non-intuitive insight: degree distribution is virtually the same for surfaces grown with different sticking coefficients!


Initial Results

Distance distributions are

clearly power-law!


Future Work How does the granularity affect? cluster-based network modeling diffusion effect


Thank you!

THE END