kMC simulations for publication "Control of Cu morphology on TaN barrier and combined Ru-TaN barrier/liner substrates for nanoscale interconnects from atomistic kinetic Monte Carlo simulations""

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 File Purpose Format metadata.json Standardized simulation parameters (domain size, process type) JSON Figure.csv Summary table linking simulations to high-level metrics CSV 📜 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 tothe European Infrastructure Nanoelectronics Program, fundedthrough the EU Horizon Europe Programme, grant no 871130.C-L. N. and M. N. were supported through the ScienceFoundation Ireland SFI–NSF China Partnership Program, grantnumber 17/NSFC/5279. 📬 Contact For questions or collaboration: Dr. Samuel Aldana Delgado Tyndall National Institute / University College Cork 📧 [e-mail]