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Meeting Materials Science & Technology 2020
Symposium Materials Design through AI Composition and Process Optimization
Presentation Title A Physics-informed AI Assistant for Atomic Layer Deposition
Author(s) Noah H. Paulson, Angel Yanguas-Gil, Steven Letourneau, Jeffery Elam
On-Site Speaker (Planned) Noah H. Paulson
Abstract Scope Atomic layer deposition (ALD) is a method for depositing thin films for applications including microelectronics, energy storage, and biomedical implants. Depositing conformal films with atomic precision relies on a self-limiting reaction between two precursors. For a new set of precursors or reactor, determining the values of parameters that result in a high quality film is a laborious manual process that relies on the experience and intuition of the operator. In this work, we present a physics-informed AI assistant that automatically tunes these processing parameters. This software, based in Bayesian optimization, leverages readings of mass-gain per cycle to sequentially suggest parameter settings and drive towards an ALD process with uniform film thickness and low precursor consumption. We demonstrate this approach for a variety of target chemistries including Al2O3, TiO2, and W.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Physics-informed AI Assistant for Atomic Layer Deposition
Accelerating the Discovery of New DP-steel Using Machine Learning-based Multiscale Materials Simulations
AI-driven Discovery of Novel High Entropy Semiconductor Alloys
Artificial Intelligence for Material and Process Design
Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science
Enabling Process Optimization Using High-throughput Machine Learning-based Image Analysis
High-fidelity Accelerated Design of High-performance Electrochemical Systems
Investigating Crystallographic Texture Control Using Laser Powder-bed Fusion Additive Manufacturing
Learning Through Domain Knowledge: A Hierarchical Machine Learning Approach Towards the Prediction of Thermoplastic Polyurethane Properties
Machine Learning Prediction of Glass Properties Informed by Synthetic Data
MeltNet: Predicting alloy melting temperature by machine learning
Multi-information Source Batch Bayesian Optimization of Alloys
NEW - Polymer Property Prediction and Design through Multi-task Learning
Realistic 3D Microstructure Generation via Generative Adversarial Networks
Statistics-based Microstructural Digital Image Correlation Method for Estimating Ex-situ Strain from Dissimilar Micrographs
Text and Data Mining for Materials Synthesis
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