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

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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.