Artificial intelligence (AI) could hold the key to developing more efficient production processes for specialty polymers used in the microelectronics industry.
A professor at the University of Nebraska-Lincoln is using AI and machine learning to understand the reaction mechanisms and structure-property behavior of microelectronics polymers synthesized in flow reactors, according to a university newsroom article published on Feb. 27.
The goal of the research is to replace traditional batch manufacturing with a more precise-flow chemistry process. The shift to continuous-flow processing would enable better control of polymer properties and structures, potentially reducing defects and improving quality, the article states.
“Taking this approach, we can improve manufacturers’ ability to produce synthetic materials while limiting defects and improving the quality of high-performance materials,” says Mona Bavarian, the research lead and assistant professor of chemical and biomolecular engineering. “The ‘continuous flow’ process also offers an opportunity for monitoring the process and controlling quality attributes through an advanced control strategy.”
Polymers used in semiconductor production undergo stringent quality requirements, which present major production challenges, the Nebraska Today author notes. A continuous-flow process allows staff to monitor raw-material quality during production and stop the process if something goes wrong rather than having to scrap an entire batch, Bavarian says.
Bavarian has received a $576,802 grant from the National Science Foundation’s Faculty Early Career Development Program for her research.