Collaborative Research: Machine Learning-assisted Ultrafast Physical Vapor Deposition of High Quality, Large-area Functional Thin Films

合作研究：机器学习辅助超快物理气相沉积高质量、大面积功能薄膜

• 批准号: 2226908
• 负责人: Xiaofeng Qian
• 金额: $ 27.26万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226908&HistoricalAwards=false
• 关键词: Collaborative; Research; Machine; Learning; assisted

This grant supports research to produce high-quality, large-area functional thin films using a machine learning-assisted ultrafast thin film manufacturing approach. Functional thin films, such as oxides, chalcogenides and nitrides, have a wide range of applications in semiconductor, communication, and energy industries. However, conventional methods for scalable manufacturing of functional thin films are time-consuming and wasteful, relying on solvents and trial-and-error approaches. The goal of this project is to apply machine learning to overcome challenges posed by structural and chemical defects associated with conventional thin film deposition, thereby improve film quality and manufacturing efficiency. Machine learning accelerates optimization of thin film growth conditions via training the experimental and computational data and speeds up the development of thin films with desired functionality. This award supports fundamental research to enable faster and cost-effective manufacturing of high-quality and large-area functional thin films for a broad range of applications in electronics, photonics, and energy conversion. Results from this project benefit the US economy and society by addressing semiconductor manufacturing and clean energy challenges facing the nation. This research involves multiple disciplines including materials science and engineering, machine learning, and advanced manufacturing. This interdisciplinary approach increases the participation of underrepresented groups in engineering research and education. The limitations of conventional thin film deposition are lack of defect control and composition manipulation, long development time, and material waste. This project applies machine learning to ultrafast physical vapor deposition to overcome these limitations and manufacture high quality, large-area functional thin films. In physical vapor deposition, film thickness, microstructure, chemical composition, and property can be engineered by tailoring the processing parameters. Closely integrating machine learning, physical property calculations, and thin film growth conditions improves film quality, shortens development cycle and reduces material waste. This research uses machine learning algorithms, such as, linear and nonlinear regression and Bayesian optimization, to train film growth and property data generated by experiment and collected from literature. Machine learning models, in conjunction with in-situ monitoring, are used to optimize growth conditions such as substrate temperature, deposition time, partial pressure, and ramping and cooling rates and achieve the targeted electronic and optical properties at a lower cost and faster development cycle. The machine learning-assisted scalable manufacturing of functional chalcogenide thin films not only enriches the materials portfolio for solar energy conversion, but also advances their applications in electronics and photonics, such as photodetectors, phototransistors, thermoelectrics, and light emission diodes. This approach can also be applied to accelerate the development of other renewable energy materials.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该赠款支持研究使用机器学习辅助超快薄膜制造方法来生产高质量的大面积功能性薄膜。功能性薄膜，例如氧化物，硫化剂和氮化物，在半导体，通信和能源工业中具有广泛的应用。但是，依赖于溶剂和反复试验的方法，用于功能性薄膜可扩展制造的常规方法是耗时且浪费的。该项目的目的是应用机器学习来克服与常规薄膜沉积相关的结构和化学缺陷所带来的挑战，从而提高膜质量和制造效率。机器学习通过训练实验和计算数据来加速薄膜生长条件的优化，并加快具有所需功能的薄膜的开发。该奖项支持基础研究，以使高质量和大面积功能性薄膜的更快，具有成本效益的制造能够在电子，光子学和能量转换中进行广泛应用。该项目的结果通过应对国家面临的半导体制造和清洁能源挑战来使美国经济和社会受益。这项研究涉及多个学科，包括材料科学和工程，机器学习和高级制造。这种跨学科的方法增加了代表性不足的群体参与工程研究和教育。常规薄膜沉积的局限性是缺乏缺陷控制和组成操纵，较长的开发时间和物质浪费。该项目将机器学习用于超快物理蒸气沉积，以克服这些局限性并制造高质量的大面积功能性薄膜。在物理蒸气沉积中，可以通过调整加工参数来设计膜厚度，微结构，化学成分和特性。紧密整合机器学习，物理特性计算和薄膜生长条件可改善膜质量，缩短开发周期并减少材料浪费。该研究使用机器学习算法，例如线性和非线性回归以及贝叶斯优化，来训练由实验并从文献中收集的膜的生长和属性数据。机器学习模型与原位监控结合使用，用于优化生长条件，例如基板温度，沉积时间，部分压和渐进率和冷却速率，并以较低的成本和更快的开发周期实现目标的电子和光学特性。机器学习辅助的功能性硫化质化薄膜的可扩展制造不仅丰富了太阳能转换的材料组合，而且还可以推动其在电子和光子学中的应用，例如光电视，光载体，光电渗透者，热电学和光发射二极管。该方法还可以应用于加速其他可再生能源材料的开发。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

• DOI: 10.48550/arxiv.2306.09549
• 发表时间: 2023-06
• 期刊: ArXiv
• 作者: Haiyang Yu;Meng Liu;Youzhi Luo;A. Strasser;X. Qian;Xiaoning Qian;Shuiwang Ji