Bayesian optimization of sputter-deposition

📌 Project Description

This repository presents a Bayesian optimization framework for guiding the sputter deposition of molybdenum thin films, targeting optimal residual stress and sheet resistance, while minimizing sensitivity to stochastic process variations. Key deposition parameters — power, pressure, and working distance — influence these properties. We apply Bayesian optimization to efficiently search the process space using a custom objective function that incorporates:

• Empirical stress and resistance data
• Prior knowledge about pressure-dependent variability

✅ Key Features

• Rapid identification of optimal deposition parameters
• Improved consistency and reproducibility of thin film properties
• Reduced experimental effort

Our results confirm that Bayesian optimization is a powerful tool for thin film process development, delivering high-performance films with controlled stress and resistance characteristics.

🧱 Project Structure

text . ├── docs/ # Sphinx or MkDocs-based documentation (API, usage, design, papers, etc.) ├── environment.yml # Conda environment specification for reproducibility ├── LICENSE # Licensing information (e.g., MIT, Apache 2.0) ├── Makefile # Automation commands (e.g., setup, test, lint, build) ├── playground/ # Prototyping area for experiments, quick tests, or notebooks (not production) ├── pyproject.toml # Project metadata and build config (PEP 621, setuptools, linting tools) ├── README.md # Project overview, usage, setup, and contribution guidelines ├── pvd_exp_demo/ # scripts to demonstrate bayesopt behavior ├── pvd_exp_run/ # scripts used during experiment design ├── utils/ # Shared utility functions and helper modules used across the project

🧩 Packaging

This project uses PEP 621-compliant configuration via pyproject.toml with setuptools.

Only utils and submodules under utils/ are included as installable packages by default. To include more:

toml [tool.setuptools.packages.find] where = ["."] include = ["utils", "utils.*", "src", "src.*", "common", "common.*"]

Badge (once setup):

markdown [![CI](https://github.com/ashriva16/bayesian-optimization-sputter-deposition/actions/workflows/ci.yml/badge.svg)](https://github.com/ashriva16/bayesian-optimization-sputter-deposition/actions)

👤 Maintainer

Ankit Shrivastava Feel free to open an issue or discussion for support.

📜 License

This project is licensed under the MIT License. See the LICENSE file for full details.

📈 Project Status

Status: 🚧 In Development — Not ready for use.

📘 References

• Cookiecutter Docs
• PEP 621
• GitHub Actions