Networking Behavior in Thin Film and Nanostructure Growth Dynamics

1. Networking Behavior in Thin Film and Nanostructure Growth Dynamics Murat Yuksel U of Nevada – Reno CSE Department yuksem@cse.unr.edu Hasan Guclu Los Alamos National Lab Complex Systems Group guclu@lanl.gov Tansel Karabacak U of Arkansas – Little Rock Applied Science Department txkarabacak@ualr.edu IEEE Nano-Net, Catania, Italy

2. Talk Outline • Motivation: Dynamic Effects in Growth • A Network Modeling Approach • Initial Results • Future Work IEEE Nano-Net, Catania, Italy

3. Motivation: Deposition Techniques IEEE Nano-Net, Catania, Italy

4. Motivation: Dynamic Effects in Growth • 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 IEEE Nano-Net, Catania, Italy

5. Motivation: Dynamic Effects in Growth root-mean-square roughness = time Theoretical models (e.g., dynamic scaling) have not been able explain growth behavior well.. IEEE Nano-Net, Catania, Italy

6. Motivation: Dynamic Effects in Growth Monte Carlo simulations have been able to explain growth behavior well.. Fundamental Questions: Can we explain the growth behavior in simple theoretical terms? Are there any universal behaviors in the growth process? IEEE Nano-Net, Catania, Italy

7. 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. A reemitting particle means that there is a “relationship” between the starting and ending points of the reemission. IEEE Nano-Net, Catania, Italy

8. A Network Modeling Approach… • cluster-based • mainly models shadowing effects • defined based on the current surface morphology • granularity matters.. • What is a “node”? • grid-based • raw grid points hill valley IEEE Nano-Net, Catania, Italy

9. A Network Modeling Approach… • 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 IEEE Nano-Net, Catania, Italy

10. A Network Modeling Approach… • 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.. IEEE Nano-Net, Catania, Italy

11. 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) IEEE Nano-Net, Catania, Italy

12. Initial Results • Grown surfaces at different thicknesses and their corresponding network models • s=0.9 IEEE Nano-Net, Catania, Italy

13. Degree distribution converges to the same for both sticking coefficients! Degree distribution converges to the same for both sticking coefficients! Initial Results • Degree distributions: • mainly Exponential, becomes power-law-like as time goes by IEEE Nano-Net, Catania, Italy

14. Initial Results Non-intuitive insight: degree distribution is virtually the same for surfaces grown with different sticking coefficients! IEEE Nano-Net, Catania, Italy

15. Initial Results Distance distributions are clearly power-law! IEEE Nano-Net, Catania, Italy

16. Future Work • How does the granularity affect? • cluster-based network modeling • diffusion effect IEEE Nano-Net, Catania, Italy

17. THE END Thank you! IEEE Nano-Net, Catania, Italy