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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.18721 |
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| _version_ | 1866917284526686208 |
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| author | Zheng, Yuanlong Blake, Connor Mravac, Layla Zhang, Fengxue Chen, Yuxin Yang, Shuolong |
| author_facet | Zheng, Yuanlong Blake, Connor Mravac, Layla Zhang, Fengxue Chen, Yuxin Yang, Shuolong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Machine Learning Approach Capturing Hidden Parameters in Autonomous Thin-Film Deposition Zheng, Yuanlong Blake, Connor Mravac, Layla Zhang, Fengxue Chen, Yuxin Yang, Shuolong Materials Science Robotics 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. |
| title | A Machine Learning Approach Capturing Hidden Parameters in Autonomous Thin-Film Deposition |
| topic | Materials Science Robotics |
| url | https://arxiv.org/abs/2411.18721 |