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Author ORCID Identifier


Campus-Only Access for One (1) Year

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Mechanical Engineering

Year Degree Awarded


Month Degree Awarded


First Advisor

Xian Du

Subject Categories

Controls and Control Theory


This work focuses on advances in roll-to-roll microcontact printing by the establishment of learning-based modeling methods that guides the monitoring and control of the continuous microcontact printing process. Microcontact printing is an attractive cost-effective method of patterning micro- and nano-scale features via selective mechanical contact on flexible web substrates using stamps. Adapting microcontact printing to continuous, roll-to-roll platforms facilitates applications such as flexible electronics and wearables. However, a limitation of present continuous printing processes is that in-line metrology is unavailable for process monitoring and control. Therefore, this work aims to develop in-line metrology for print pattern quality monitoring of nano-thin monolayer print processes and use neural-network-based predictive control to control the printing processes. First, a real-time prediction of web tension based on contactless sensing and deep learning is developed. Second, a neural-network-based adaptive model predictive control for a flexure-based roll-to-roll contact printing system is designed. To further study the mechanisms of the microcontact printing process, an offline consistent optical surface inspection based on open environment droplet size-controlled condensation figures for print pattern quality monitoring is developed. Inline metrology for microcontact printing quality monitoring based on condensation figures is then applied. Finally, a case study of a novel autofocus method is presented to show the possibility of applying deep learning-based model predictive control in the control of the roll-to-roll microcontact printing process.


Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.