FMSG: Integrating Artificial Intelligence in Chemical Vapor Deposition for In-situ Predictive Crystal Growth Manufacturing.

FMSG：将人工智能集成到化学气相沉积中，用于原位预测晶体生长制造。

• 批准号: 2036737
• 负责人: Qi Fan
• 金额: $ 50万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036737&HistoricalAwards=false
• 关键词: FMSG; Integrating; Artificial; Intelligence; Chemical

In electronics, large crystal of silicon is used as the basis for semiconductor computer chips and switching devices for electric grid applications. The efficiency of electronic devices is dependent on the perfection of the crystals as it offers better control of electron flow without loss. Different types of semiconductor crystals, like diamond, can outperform silicon but are essentially unavailable for use. The current project proposes to use artificial intelligence on the data generated and collected during crystal growth to predict parameters instead of trial and error for growth of defect free crystals. The use artificial intelligence will assess the data generated during the growth process itself, the current state of crystal growth, and predict the growth results. Development and integration of deep learning artificial intelligence architectures in the Chemical Vapor Deposition process will make growth predictions more accurate and add defect assessment to the prediction for manufacturing of diamond material System. Outcome of the project will accelerate the development cycles and reduce costs for manufacturing processes which will be adaptable to a broad range of crystal growth processes for electronics. Concepts developed in the project will be integrated into existing courses, capstone projects will be designed for students, and education modules will be developed for training operators. A course in data collection, handling, and interpretation will be developed for vocational workers to understand, adapt, and team with artificial intelligence augmented manufacturing machines in the work environment. The course will be disseminated to manufacturing community by partnering with the Automation Alley, an industry manufacturing consortium.The proposed project will design and develop a holistic artificial intelligence platform to solve the problems of traditional approaches for growth of large-scale crystalline diamond material system. The approach will focus on increasing the resolution of image collection and training the program to resolve problems with spatio-temporal data, including: (1) checkerboard artifacts, (2) lack of photo-realism, and (3) inability to prevent feature loss, while maintaining a large frame resolution. Further, the artificial intelligence architectures developed for this project will be merged into solutions for frame prediction based on input time series parameters like temperature and defects to achieve state-of-the-art accuracy metrics in growth state prediction. The large scale and defect free diamond material system is one of the most challenging and holds the promise of revolutionizing power device technology. The enhanced predictive capabilities proposed in this project derived from higher resolution images and incorporation of microscope defect data will enable in-process control of the evolving growth process for diamond and will lead the way for fully automated process control of crystal growth processes for manufacturing.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.

在电子产品中，硅的大晶体被用作半导体计算机芯片和用于电网应用的开关设备的基础。电子设备的效率取决于晶体的完美，因为它可以更好地控制电子流，而不会损失。钻石等不同类型的半导体晶体可以胜过硅，但基本上不可用。当前的项目建议对晶体生长过程中生成和收集的数据使用人工智能来预测参数，而不是反复试验，而不是自由晶体的生长。使用人工智能将评估生长过程本身，晶体生长的当前状态中产生的数据，并预测增长结果。深度学习人工智能体系结构在化学蒸气沉积过程中的开发和整合将使增长预测更加准确，并为制造钻石材料系统的预测增加缺陷评估。 该项目的结果将加速开发周期并降低制造过程的成本，这些过程将适应电子产品的广泛晶体生长过程。该项目中开发的概念将集成到现有课程中，Capstone项目将为学生设计，并将为培训运营商开发教育模块。 将开发数据收集，处理和解释的课程，以供职业工人在工作环境中使用人工智能增强制造机的理解，适应和团队。该课程将通过与自动化巷（Automation Alley）合作（一个行业制造联盟）将其传播到制造业社区。拟议的项目将设计和开发整体人工智能平台，以解决传统方法的问题，以增长大型晶体钻石材料系统。该方法将着重于增加图像收集和培训程序以解决时空数据的问题，包括：（1）棋盘伪影，（2）缺乏照相真实主义，以及（3）无法防止特征丢失，同时保持大型框架分辨率。此外，根据输入时间序列参数（如温度和缺陷），将为该项目开发的人工智能体系结构将合并为框架预测的解决方案，以实现生长状态预测中的最新准确度指标。大规模和缺陷的无钻石材料系统是最具挑战性的钻石材料系统之一，并具有革新电力设备技术的希望。该项目中提出的增强的预测能力从较高的分辨率图像和显微镜缺陷数据纳入将能够对钻石不断发展的增长过程进行过程控制，并将为制造业的晶体增长过程完全自动化的过程控制带来途径。该奖项奖励NSF的法定任务反映了通过评估范围的构成群体的支持者的范围。