Experimental Platform and Digital Twin for AI-Driven Materials Optimization and Discovery for Microelectronics Using Atomic Layer Deposition

Abstract

Atomic layer deposition (ALD) is a thin film growth technique that is key for both microelectronics and energy applications. Its step-by-step nature and its integration into fully automated clusters with wafer handling systems make is an ideal tool for AI-driven optimization and discovery. In this work we describe an experimental setup and digital twin of an ALD reactor coupled with in-situ characterization techniques that we have developed as a platform for the development and validation of novel algorithms for self-driving labs. Preliminary results show that it is possible to achieve a 100-fold reduction in the time required to optimize new processes. Finally we share some of the lessons learned during the design and validation of our self-driven thin film growth tool.

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Cite

Text

Yanguas-Gil et al. "Experimental Platform and Digital Twin for AI-Driven Materials Optimization and Discovery for Microelectronics Using Atomic Layer Deposition." NeurIPS 2022 Workshops: AI4Mat, 2022.

Markdown

[Yanguas-Gil et al. "Experimental Platform and Digital Twin for AI-Driven Materials Optimization and Discovery for Microelectronics Using Atomic Layer Deposition." NeurIPS 2022 Workshops: AI4Mat, 2022.](https://mlanthology.org/neuripsw/2022/yanguasgil2022neuripsw-experimental/)

BibTeX

@inproceedings{yanguasgil2022neuripsw-experimental,
  title     = {{Experimental Platform and Digital Twin for AI-Driven Materials Optimization and Discovery for Microelectronics Using Atomic Layer Deposition}},
  author    = {Yanguas-Gil, Angel and Letourneau, Steve and Paulson, Noah and Elam, Jeffrey W},
  booktitle = {NeurIPS 2022 Workshops: AI4Mat},
  year      = {2022},
  url       = {https://mlanthology.org/neuripsw/2022/yanguasgil2022neuripsw-experimental/}
}