The University of Massachusetts Amherst
University of Massachusetts Amherst

Search Google Appliance


Du’s NSF Project Proposes to Develop Novel Sensing and Control Technology for a Roll-to-roll Printing Process

Xian Du

Xian Du

Xian Du of the Mechanical and Industrial Engineering Department has received a two-year National Science Foundation (NSF) grant of $498,764 to support his research into a novel sensing and control technology for a roll-to-roll printing process.

Du’s research project, supported through the NSF Grant Opportunities for Academic Liaison with Industry (GOALI) award program, is meant to establish a technological base for the development of a multiscale in-line metrology platform that, according to Du, could promote “both the invention and manufacturing of revolutionary new flexible electronics products, giving the U.S. a competitive edge in the global economy.” The project title is “Monitoring and Control of Roll-to-roll Printing of Flexible Electronics through Multiscale In-Line Metrology.”

According to Du’s proposal, roll-to-roll (R2R) printing of flexible electronics involves fabricating thin electronic structures ranging in feature size from nanometer to millimeter along a continuously moving flexible substrate at speeds of meters per minute.

“The roll-to-roll printing technique offers the potential to radically shift the cost structure for large-area nanostructured devices and enables versatile applications of flexible functional systems,” says Du. “However, a limitation of present continuous printing processes is that in-line metrology is unavailable for process monitoring and control.”

Du adds that “In this study, ultra-thin print patterns along a continuously moving flexible web are imaged, registered, and measured in real-time. This process control system can be adapted for different roll-to-roll printing processes for a variety of applications such as industrial internet-of-things and infrastructure health-monitoring.”

However, as Du says, “numerous research gaps must be met for these printing processes to be scaled up to industrial scale.”

The research gaps include invisibility of the ultra-thin patterns in a normal optical imaging environment, loss of pattern registration, optical limits on field-of-view and resolution, and inability of conventional control methods to capture high-order dynamics and nonlinearity in these printing processes.

“To meet these research gaps,” says Du, “this project develops in-line metrology for print pattern quality monitoring of nano-thin monolayer print processes, investigates high-resolution imaging and registration of large-area nano- and micron-scale patterns, and explores the deep-learning-based predictive control of R2R printing processes by integrating in-line multiscale metrology and process modeling.”

According to Du, the in-line monolayer pattern is imaged using real-time water vapor condensation figures and synchronous image processing. The predictive model is a recurrent conditional deep predictive neural network that incorporates short-term and long-term nonlinearly dynamic print input-output responses to optimize prediction errors.

“To address the broad and complex array of problems that are involved in R2R print process control and its scale-up to industrial applications,” says Du, “a close collaboration with the GOALI partner has been established to guide the research efforts and test the in-line metrology platform.”

This project also involves training students at the industrial partner facility that has roll-to-roll nano-manufacturing capabilities. It incorporates fundamental research results into undergraduate and graduate courses to advance the students' interests and skills in solving practical engineering problems. (August 2019)