CAREER: Modeling the Roll-to-Roll Soft Lithography Printing Process Through Deep Learning and Real-time Sensing

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

This Faculty Early Career Development (CAREER) grant focuses on advances in roll-to-roll soft lithography by establishing a learning-based modeling method that guides the design and control of continuous microcontact printing processes and investigates continuous pattern formation mechanisms. Microcontact soft lithography is an attractive cost-effective method of patterning meter square areas of micro- and nano-scale features via selective mechanical contact on flexible substrates using stamps. Adapting microcontact printing to continuous, roll-to-roll platforms facilitates applications such as flexible electronics and wearables. The fidelity of the transferred pattern in soft lithography is dependent on successful mechanical contact and control of material transfer at the stamp-substrate interface. However, the underlying microfeature evolution in commonly fabricated structures in roll-to-roll microcontact printing is not yet fully understood to guide the successful design and control of the printing process. This project studies a novel modeling approach for microcontact print pattern formation with real-time learning from the patterning processes while linking the pattern formation mechanisms with print process variations and defects for quality control. The research is complemented by the development of a multi-disciplinary curriculum combined with research in roll-to-roll printing and the creation of self-contained hands-on educational kits to encourage young students at various educational levels to pursue careers in manufacturing. The goal of this research is to understand the fundamental mechanics of microcontact printing through deep learning and establishing a scientific basis for roll-to-roll soft lithography. Towards this goal, the research objectives are: (1) Investigate a physical mechanics model of contact regions for the design and control of roll-to-roll microcontact printing; (2) Establish an in-line vision-force-deformation sensing network for assessing print geometry; and (3) Model real time stamp microfeature pattern-pressure-deformation behavior through deep learning. The printed pattern images are systematically segmented and converted to a single geometric variable which is synchronized and integrated with the force and displacement data for describing the state of print. A deep learning architecture models the fidelity of a print pattern geometry by measurement of the print variables and contact geometries. The project seeks answers to the following questions: (i) What is the precise interpretable relationship between print variables and contact geometry; and (ii) What are the roles of contact geometries, material properties, and roll-to-roll printing parameters in the formation of pattern geometry. The overarching focus is to achieve a deep understanding of the deformation behavior of microcontact stamp and the formation mechanisms of print geometry. The project enriches the knowledge base for soft lithography modeling, real-time sensing, deep learning, and design and control of roll-to-roll print process and contributes to the advancements in intelligent manufacturing. 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.