This Grant Opportunities for Academic Liaison with Industry (GOALI) award supports research that contributes novel sensing and control technology for a roll-to-roll printing process, promoting both the invention and manufacturing of revolutionary new flexible electronics products, giving the U.S. a competitive edge in the global economy. Roll-to-roll 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. However, a limitation of present continuous printing processes is that in-line metrology is unavailable for process monitoring and control. This research establishes a technological base for the development of a multiscale in-line metrology platform. 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. This project involves training students at the industrial partner facility that has roll-to-roll nanomanufacturing capabilities. It incorporates fundamental research results into undergraduate and graduate courses to advance the students' interests and skills in solving practical engineering problems.
Many lab-scale roll-to-roll (R2R) printing processes have been shown to have the ability to print flexible electronics with resolutions ranging from nanometers to millimeters. However, 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 R2R printing processes. To meet these research gaps, 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. 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, a close collaboration with the GOALI partner has been established to guide the research efforts and test the in-line metrology platform.
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.
