CAREER: Continuous Flow Chemistry of Microelectronics Polymers via Combined Physics-based and Machine Learning Models

职业：通过基于物理和机器学习相结合的微电子聚合物的连续流动化学

• 批准号: 2238147
• 负责人: Mona Bavarian
• 金额: $ 57.68万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238147&HistoricalAwards=false
• 关键词: CAREER; Continuous; Flow; Chemistry; Microelectronics

Microelectronics polymers are essential in the development of semiconductor technology. Precise microchip manufacturing hinges on high-quality polymer inputs. However, current methods for producing microelectronics polymers do not satisfy all the requirements of high throughput manufacturing due to challenges with scale-up, process control, purity, quality control, and production time. To tackle some of these challenges, the PI plans to create a platform for data-driven design of polymer materials and their manufacturing processes, as well as optimal operation and control of these processes. The PI’s unique approach is to replace the current practice of high-volume batch reactors with continuous flow reactors that allow for the precise control of polymer properties and structure. The proposed platform is eco-friendly, as it promises to reduce the carbon footprint, decrease operating costs, and limit the amount of inferior, unusable materials generated during the manufacturing. The program will also advance knowledge across several other fields, as the modeling and manufacturing knowledge gained from this project will be applicable to other specialty polymers. In addition to training graduate and undergraduate students in research, the project will contribute to the development of course materials on advanced manufacturing and will involve outreach activities in the form of high school teacher trainings focused on advanced manufacturing and microelectronics.This project will address several fundamental research problems related to microelectronics polymers manufacturing. Researchers will investigate the effect of reaction conditions, process parameters, and quality of raw materials on the microelectronics polymers properties, and quality attributes. Experimental and theoretical/computational studies will be performed to enable the continuous flow chemistry of microelectronics polymers. Combined physics-based and machine learning process models will be developed to predict the dynamics of the processes that produce these polymers. These models will account for heat, mass, and momentum transfer and reaction kinetics to predict process variables such as polymer molecular weight and polymer compositions. These models will also provide better understanding of reaction mechanisms and structure-property relationship of microelectronics polymers synthesized in flow reactors. Therefore, it is expected that the models will reduce the chemical dimensional space to expedite the discovery of new microelectronics polymers that meet the required molecular structure-property relationships. A deliverable of this project is a manufacturing platform that uses online information from reaction monitoring and in operando spectroscopy for feedback control of polymer quality attributes, allowing for the efficient continuous and autonomous production of microelectronics polymers.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.

微电子聚合物对于半导体技术的开发至关重要。精确的微芯片制造在高质量聚合物输入上取决于。但是，由于通过扩展，过程控制，纯度，质量控制和生产时间的挑战，当前生产微电子聚合物的方法无法满足高吞吐量制造的所有要求。为了应对其中一些挑战，PI计划为数据驱动的聚合物材料及其制造工艺设计平台，以及对这些过程的最佳操作和控制。 PI的独特方法是用连续的流动反应器代替当前的大容量批处理反应器的实践，从而可以精确控制聚合物性能和结构。提出的平台非常适合环保，因为它有望减少碳足迹，降低运营成本并限制制造过程中产生的劣等，无法使用的材料的量。该计划还将推进其他几个领域的知识，因为该项目从该项目中获得的建模和制造知识将适用于其他专业聚合物。除了培训研究生和本科研究生外，该项目还将有助于开发高级制造业的课程材料，并涉及以高中教师培训的形式进行外展活动。该项目将解决与微电脑聚合物制造有关的几个基本研究问题。研究人员将研究反应条件，过程参数和原材料质量对微电子聚合物特性以及质量属性的影响。将进行实验和理论/计算研究，以实现微电子聚合物的连续流化学。将开发基于物理和机器学习过程模型的组合，以预测产生这些聚合物的过程的动力学。这些模型将考虑热，质量和动量转移和反应动力学，以预测过程变量，例如聚合物分子量和聚合物组成。这些模型还将更好地理解反应器中合成的微电子聚合物的反应机理和结构 - 质地关系。因此，预计这些模型将减少化学尺寸空间，以加快符合所需分子结构 - 特性关系的新微电子聚合物的发现。 A deliverable of this project is a manufacturing platform that uses online information from reaction monitoring and in operando spectroscopy for feedback control of polymer quality attributes, allowing for the efficient continuous and autonomous production of microelectronics polymers.This award reflects NSF's statutory mission and has been deemed precious of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.