EAGER Real-D: Real-time Data-Based Modeling and Control of Plasma-Enhanced Atomic Layer Deposition

EAGER Real-D：等离子体增强原子层沉积的基于数据的实时建模和控制

• 批准号: 1836518
• 负责人: Panagiotis Christofides
• 金额: $ 20万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1836518&HistoricalAwards=false
• 关键词: EAGER; Real; time; Data; Based

CBET-1836518PI: Christofides, Panagiotis D. Next generation electronic devices require the use of improved materials and very precise material processing techniques. To reduce feature sizes and improve energy efficiency, the devices employ extremely thin layers, high aspect ratios, atomically-sharp interfaces, or any combination thereof. Due to inherent difficulties in applying real-time in situ monitoring and control of film properties, factory operators typically rely on batch thin film deposition and etching cycles, followed by scanning electron microscopy (SEM) or x-ray photoelectron spectroscopy (XPS) characterization of the deposited thin films to determine the effect of controllable reactor parameters on the resulting product. This empirical approach reduces productivity and fails to provide complete data on the behavior and operation of chambered reactors common to thin-film processing. Multiscale computational fluid dynamics (CFD) modeling provides a means for addressing these concerns by reducing empiricism and allowing the development of complete data sets that can be used to optimize and control reactor operating conditions in real time. The proposed research is exploratory in nature and focuses on developing a multiscale CFD modeling and control framework that can enable control of thin film manufacturing via plasma-enhanced atomic layer deposition (PE-ALD) to optimize in real time the microstructure of the deposited thin films. CFD models have been shown to capture the complex reaction and transport phenomena present within plasma charged reactors, while microscopic models, typically based on kinetic Monte Carlo (kMC) algorithms, have successfully reproduced the surface features of deposited films. A multiscale CFD model encompassing both domains would represent a significant step forward in understanding of thin-film processing via PE-ALD and could allow for improved real-time online monitoring and control of chambered reactor operations. However, such a model will be unsuitable for the development of real-time optimizers and model-based controllers because CFD simulations are generally computationally demanding and cannot be linked to online model predictive control schemes. Nonetheless, the proposed multiscale CFD model can be used as a risk-free and effective tool to investigate previously unexplored operating conditions of the PE-ALD reactor and create a database which can be utilized to derive a computationally efficient data-driven model for PE-ALD real-time control. The multiscale CFD model which will be developed will allow for the application of a novel, computationally efficient data-based Bayesian artificial neural network (ANN). Furthermore, data-driven models developed using the reactor model will form the basis for real-time process optimization and control. The data-based model will be used to develop real-time operational decision strategies for PE-ALD that reduce thin film layer deposition times, which constitutes a necessary step for adoption of this technology. The proposed methodology can form a basis for real-time optimization and control of next generation deposition systems and may be adapted to a wide range of industrial processes. Dissemination of research results will include web-based access to a database and results repository. In addition to training a PhD student, the PI plans to integrate research results into the curriculum through the inclusion of CFD modeling and its integration with control in the Advanced Process Control course that the PI offers to both graduate and undergraduate UCLA students, as well as through the integration of CFD concepts and tools into the undergraduate numerical methods, process design and process control core courses.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.

CBET-1836518 PI：Christofides，Panagiotis D. 下一代电子设备需要使用改进的材料和非常精确的材料加工技术。为了减小特征尺寸并提高能量效率，器件采用极薄的层、高纵横比、原子级尖锐的界面或其任何组合。由于应用实时原位监测和控制膜性质的固有困难，工厂操作者通常依赖于批量薄膜沉积和蚀刻循环，随后是沉积薄膜的扫描电子显微镜（SEM）或X射线光电子能谱（XPS）表征，以确定可控反应器参数对所得产品的影响。这种经验方法降低了生产率，并且不能提供关于薄膜加工中常见的腔室反应器的行为和操作的完整数据。多尺度计算流体动力学（CFD）建模提供了一种解决这些问题的方法，通过减少非线性，并允许开发完整的数据集，可用于优化和控制反应堆的操作条件，在真实的时间。拟议的研究是探索性的性质，并侧重于开发一个多尺度的CFD建模和控制框架，可以通过等离子体增强原子层沉积（PE-ALD），以优化薄膜制造的控制在真实的时间的沉积薄膜的微观结构。计算流体力学模型已被证明捕捉复杂的反应和传输现象，目前在等离子体充电反应器，而微观模型，通常基于动力学蒙特卡罗（kMC）算法，已成功地再现了沉积膜的表面特征。涵盖这两个领域的多尺度CFD模型将代表通过PE-ALD理解薄膜加工的重要一步，并且可以改进室式反应器操作的实时在线监测和控制。然而，这样的模型将不适合于实时优化器和基于模型的控制器的开发，因为CFD模拟通常需要计算并且不能与在线模型预测控制方案相关联。尽管如此，所提出的多尺度CFD模型可以用作无风险和有效的工具，以调查以前未探索的PE-ALD反应器的操作条件，并创建一个数据库，该数据库可以用于导出用于PE-ALD实时控制的计算高效的数据驱动模型。将开发的多尺度CFD模型将允许应用一种新的、计算效率高的基于数据的贝叶斯人工神经网络（ANN）。此外，使用反应器模型开发的数据驱动模型将成为实时过程优化和控制的基础。基于数据的模型将用于开发PE-ALD的实时操作决策策略，以减少薄膜层沉积时间，这是采用该技术的必要步骤。所提出的方法可以形成下一代沉积系统的实时优化和控制的基础，并且可以适用于广泛的工业过程。研究成果的传播将包括通过网络访问数据库和成果储存库。除了培养一名博士生，PI还计划通过将CFD建模及其与PI为加州大学洛杉矶分校研究生和本科生提供的高级过程控制课程中的控制集成，以及通过将CFD概念和工具集成到本科数值方法中，过程设计和过程控制核心课程。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。