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

在电子学中，大晶体硅被用作半导体计算机芯片和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好性，因为它提供了更好的无损耗电子流动控制。不同类型的半导体晶体，如钻石，可以比硅性能更好，但基本上无法使用。目前的项目建议使用人工智能处理晶体生长过程中产生和收集的数据来预测参数，而不是反复尝试生长无缺陷晶体。使用人工智能将评估生长过程本身产生的数据、晶体生长的当前状态，并预测生长结果。在化学气相沉积过程中开发和集成深度学习人工智能体系结构将使生长预测更加准确，并为金刚石材料系统的制造预测增加缺陷评估。该项目的成果将加快开发周期并降低制造工艺的成本，这将适用于电子产品的广泛晶体生长工艺。该项目中提出的概念将被纳入现有课程，顶峰项目将为学生设计，并将为培训操作员开发教育模块。将为职业工人开发一门关于数据收集、处理和解释的课程，以了解、适应并在工作环境中与人工智能增强制造机器合作。该课程将通过与工业制造联盟Automation Alley合作向制造业传播。拟议的项目将设计和开发一个全面的人工智能平台，以解决传统方法生长大型水晶钻石材料系统的问题。该方法将专注于提高图像采集的分辨率并训练程序来解决时空数据的问题，包括：(1)棋盘状伪影，(2)缺乏照片真实感，(3)在保持大的帧分辨率的同时无法防止特征丢失。此外，为该项目开发的人工智能架构将被合并到基于输入时间序列参数(如温度和缺陷)的帧预测解决方案中，以实现最先进的增长状态预测精度指标。大尺寸和无缺陷的金刚石材料系统是最具挑战性的系统之一，有望给电力设备技术带来革命性的变化。该项目建议增强的预测能力源自更高分辨率的图像和结合显微镜缺陷数据，这将实现对钻石生长过程的过程中控制，并将引领用于制造的晶体生长过程的全自动化过程控制。该奖项反映了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。

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.