A Machine Learning Approach Capturing Hidden Parameters in Autonomous Thin-Film Deposition

TL;DR

This paper presents an autonomous thin-film deposition system that uses machine learning and active learning to optimize film quality despite hidden parameters, reducing time and labor in material fabrication.

Contribution

It introduces a fully autonomous vapor deposition system integrating in-situ spectroscopy, robotic handling, and Gaussian Process Regression to handle hidden parameters and optimize film quality.

Findings

Achieved silver thin films with optical properties within 2.5% of targets

Reduced deposition attempts to an average of 2.3 per film

Demonstrated effective handling of hidden parameter variations

Abstract

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.