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

基本信息

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

项目摘要

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.

这笔赠款支持使用机器学习辅助的超快薄膜制造方法生产高质量、大面积功能薄膜的研究。氧化物、硫族化物和氮化物等功能薄膜在半导体、通信和能源行业有着广泛的应用。然而，功能性薄膜可规模化制造的传统方法依赖于溶剂和试错方法，既耗时又浪费。该项目的目标是应用机器学习来克服与传统薄膜沉积相关的结构和化学缺陷带来的挑战，从而提高薄膜质量和制造效率。机器学习通过训练实验和计算数据加速薄膜生长条件的优化，并加速具有所需功能的薄膜的开发。该奖项支持基础研究，以实现更快、更具成本效益地制造高质量、大面积功能薄膜，以应用于电子、光子和能源转换领域的广泛应用。该项目的成果通过解决美国面临的半导体制造和清洁能源挑战，使美国经济和社会受益。该研究涉及材料科学与工程、机器学习、先进制造等多个学科。这种跨学科方法增加了代表性不足的群体对工程研究和教育的参与。传统薄膜沉积的局限性是缺乏缺陷控制和成分控制、开发时间长和材料浪费。该项目将机器学习应用于超快物理气相沉积，以克服这些限制并制造高质量、大面积的功能薄膜。在物理气相沉积中，可以通过调整工艺参数来设计薄膜厚度、微观结构、化学成分和性能。将机器学习、物理性能计算和薄膜生长条件紧密结合，可提高薄膜质量、缩短开发周期并减少材料浪费。本研究使用线性和非线性回归以及贝叶斯优化等机器学习算法来训练通过实验生成并从文献中收集的薄膜生长和性能数据。机器学习模型与原位监控相结合，用于优化生长条件，例如基板温度、沉积时间、分压以及升温和冷却速率，并以更低的成本和更快的开发周期实现目标电子和光学特性。机器学习辅助的功能性硫族化物薄膜的可扩展制造不仅丰富了太阳能转换的材料组合，而且还推进了其在电子和光子学中的应用，例如光电探测器、光电晶体管、热电学和发光二极管。这种方法还可用于加速其他可再生能源材料的开发。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery

材料发现不确定性下的知识驱动学习、优化和实验设计

DOI：
10.1016/j.patter.2023.100863
发表时间：
2023-11
期刊：
Patterns
影响因子：
6.5
作者：
Qian, Xiaoning;Yoon, Byung;Arróyave, Raymundo;Qian, Xiaofeng;Dougherty, Edward R.
通讯作者：
Dougherty, Edward R.

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

QH9：QM9 分子的量子哈密顿预测基准

DOI：
10.48550/arxiv.2306.09549
发表时间：
2023-06-15
期刊：
ArXiv
影响因子：
0
作者：
Haiyang Yu;Meng Liu;Youzhi Luo;A. Strasser;X. Qian;Xiaoning Qian;Shuiwang Ji
通讯作者：
Shuiwang Ji