Kinetic Monte Carlo Simulations of Cu Metal Growth

Growth direction: (111)  
Processes: Homoepitaxial growth on TaN and TaN modified with Ru (TaN, Ru25 and Ru50 modelled by modifying the activation energies), and post-deposition thermal vacuum annealing

📄 Overview

This repository and dataset accompany the manuscript, containing the source code, input parameters, simulation outputs, and analysis scripts required to reproduce the results:  
"Control of Cu morphology on TaN barrier and combined Ru-TaN barrier/liner substrates for nanoscale interconnects from atomistic kinetic Monte Carlo simulations"  
Authors: Samuel Aldana, Cara-Lena Nies and Michael Nolan  
Journal: Nanoscale, Royal Society of Chemistry  

• DOI: 10.1039/d4nr04505j
• arXiv: 2410.06133

The simulations utilize an open-source kinetic Monte Carlo simulator (kMC) simulator developed in Python by Dr. Samuel Aldana Delgado, designed to model thin-film growth and thermal vacuum annealing dynamics.

• Code repository: https://github.com/aldanads/Kinetix
• Code version (commit): 1a4689f

This dataset is structured to support reproducibility and multiscale modeling in nanofabrication and materials design.

📂 Directory Structure

Outputs/
├── DFT/                                                   # DFT data (activation energies)
└── kMC/                                                 # kMC data 
    ├── time_evolution/                             # Simulation for time evolution 
    ├── partial_pressure/                           # Simulation under high pressure conditions
    ├── annealing/                                    # Thermal vacuum annealing after deposition
    └── statistical_10sim/                          # 10 runs per condition for statistics
        └── <Substrate>/                            # e.g., TaN, Ru25, Ru50
            └── <P=X>/                                 # Pressure (Pa): P=0.1, P=0.5, etc
                   └── <TXXX>/                        # Temperature (K): T = 300, 500, 700K (for annealing or deposition: check metadata)  
                         └── Sim_*/                      # Individual simulation folders
                               ├── metadata.json    # Metadata summary
                               ├── Program/             
                               │   ├── *.py                 # Source code
                               │   └── variables.pkl│ # Last state when simulation finishes
├── Figures/
│ ├── *.ipynb                                             # Jupyter notebooks to reproduce manuscript figures
│ └── Processed_data/
│ │ ├── *.csv files with processed data 
│
├── manuscript/
│ ├── manuscript.pdf
│ └── Supporting_information/                 # Additional data and methods
│
└── README.md

🔑 Key Files for Reuse

📜 Licensing

• Simulation code: MIT License
• Dataset (metadata, results, trajectories): CC BY 4.0
    → You are free to share and adapt the data, provided you give appropriate credit.

🙏 Acknowledgments

M. N. and S. A. received support from the ASCENT + Access to
the European Infrastructure Nanoelectronics Program, funded
through the EU Horizon Europe Programme, grant no 871130.
C-L. N. and M. N. were supported through the Science
Foundation Ireland SFI–NSF China Partnership Program, grant
number 17/NSFC/5279.

📬 Contact

For questions or collaboration:  
Dr. Samuel Aldana Delgado 
Tyndall National Institute / University College Cork  
📧 samuel.delgado@tyndall.ie