2024/11/27 by Yuanlong Zheng, Zheng, Yuanlong, Layla Mravac +8
Materials Science · #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2411.18721
openalex publication_date 2024/11/27 · openalex created_date 2024/12/05 · openalex updated_date 2026/07/28
The integration of machine learning and robotics into thin film deposition is transforming material discovery and optimization. However, challenges remain in achieving a fully autonomous cycle of deposition, characterization, and decision-making. Additionally, the inherent sensitivity of thin film growth to hidden parameters such as substrate conditions and chamber conditions can compromise the performance of machine learning models. In this work, we demonstrate a fully autonomous physical vapor deposition system that combines in-situ optical spectroscopy, a high-throughput robotic sample handling system, and Gaussian Process Regression models. By employing a calibration layer to account for hidden parameter variations and an active learning algorithm to optimize the exploration of the parameter space, the system fabricates silver thin films with optical reflected power ratios within 2.5% of the target in an average of 2.3 attempts. This approach significantly reduces the time and labor required for thin film deposition, showcasing the potential of machine learning-driven automation in accelerating material development.