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

这笔赠款支持使用机器学习辅助的超快薄膜制造方法来生产高质量、大面积功能薄膜的研究。氧化物、硫化物、氮化物等功能薄膜在半导体、通讯、能源等领域有着广泛的应用。然而，传统的可规模化制造功能薄膜的方法既耗时又浪费，依赖于溶剂和反复试验的方法。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来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、更具成本效益的高质量和大面积功能薄膜的制造，用于电子、光子学和能源转换的广泛应用。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。这项研究涉及多个学科，包括材料科学与工程、机器学习和先进制造。这种跨学科的方法增加了代表不足的群体对工程研究和教育的参与。传统的薄膜沉积存在缺陷控制和成分控制不足、显影时间长、材料浪费等缺点。该项目将机器学习应用于超高速物理气相沉积，以克服这些限制，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的厚度、微结构、化学成分和性能。将机器学习、物性计算和薄膜生长条件紧密结合起来，提高了薄膜质量，缩短了开发周期，减少了材料浪费。本研究使用机器学习算法，如线性、非线性回归和贝叶斯优化，训练实验产生的薄膜生长和性能数据，并从文献中收集。机器学习模型与现场监测相结合，用于优化生长条件，如衬底温度、沉积时间、分压、倾斜和冷却速度，并以更低的成本和更快的开发周期实现目标的电子和光学性能。机器学习辅助的硫系功能薄膜的可扩展制造不仅丰富了太阳能转换的材料组合，而且促进了其在电子和光子学中的应用，如光电探测器、光电晶体管、热电材料和发光二极管。这种方法也可以用于加速其他可再生能源材料的开发。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.