A University of Nebraska–Lincoln scientist is using artificial intelligence to develop an advanced manufacturing platform for polymer coatings.
Polymer coatings are essential components in the fabrication of many electronic devices, including communications, computing, health care, military systems, transportation, energy and countless other applications. Polymers, especially those used in semiconductor technology, have stringent quality requirements, with scale-up, purity and production time presenting major challenges to their manufacturing.
Mona Bavarian, assistant professor of chemical and biomolecular engineering, has received a $576,802 grant from the National Science Foundation’s Faculty Early Career Development Program for her research to meet these exacting standards.
Guided by artificial intelligence, Bavarian aims to replace traditional batch manufacturing with a more precise flow chemistry process. The switch would allow for better control of polymer properties and structures, reducing the defects and improving overall quality.
In line with the theme of continuous polymerization of specialty polymers, the proposed research focuses on understanding the reaction mechanisms and structure-property behavior of microelectronics polymers synthesized in flow reactors. Bavarian will combine first principles and machine learning models to gain this information.
“Taking this approach, we can improve manufacturers’ ability to produce synthetic materials while limiting defects and improving the quality of high-performance materials,” Bavarian said. “The ‘continuous flow’ process also offers an opportunity for monitoring the process and controlling quality attributes through an advanced control strategy.”
This approach would allow for the high throughput manufacturing of specialty polymers with qualities unattainable by traditional batch manufacturing, she said.
If something goes awry with ingredients in batch manufacturing, the entire contents may be ruined. The flow process uses advanced control techniques and allows production staff to monitor the quality of raw materials and stop the process if something goes wrong.
Bavarian’s approach aims to reduce waste in the manufacturing process, making it more eco-friendly and sustainable to produce microelectronics.
“Specialty polymers require high-precision manufacturing,” she said. “The semiconductor industry has a high need for these materials, especially as electronic devices are becoming smaller and widely used in a variety of products.”
Modeling and manufacturing knowledge gained from this research should be applicable to other specialty polymers, Bavarian said.
As is required with CAREER grants, this project includes an education and outreach component. Bavarian will train doctoral and undergraduate students, creating 10 unique research opportunities for undergrads from underrepresented groups and contribute to curriculum development for four high school teachers.
“We want to spark interest in microelectronics and advanced manufacturing and equip the next generation of scientists and engineers in these fields,” Bavarian said. “There is a significant need for workforce development in the advanced manufacturing semiconductor industry and also in specialty chemical companies.”
The National Science Foundation’s CAREER award supports pre-tenure faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research.
