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

该学院早期职业发展（CAREER）资助的重点是通过建立一种基于学习的建模方法，指导连续微接触印刷工艺的设计和控制，并研究连续图案形成机制，来实现卷对卷软光刻的进步。微接触软光刻是一种具有吸引力的成本效益的方法，通过使用印模在柔性衬底上的选择性机械接触来图案化微米和纳米尺度特征的平方米区域。使微接触印刷适应连续的卷对卷平台，有助于柔性电子产品和可穿戴设备等应用。软光刻中转移图案的保真度取决于成功的机械接触和在印模-基底界面处的材料转移的控制。然而，在通常制造的结构中，在辊对辊微接触印刷的底层微特征的演变尚未完全理解，以指导成功的设计和控制的印刷过程。该项目研究一种新型的微接触印刷图案形成建模方法，通过对图案化过程进行实时学习，同时将图案形成机制与印刷工艺变化和缺陷联系起来，以进行质量控制。该研究通过开发一个多学科的课程，结合卷对卷印刷的研究和创建独立的实践教育套件，以鼓励不同教育水平的年轻学生从事制造业。本研究的目标是通过深度学习了解微接触印刷的基本力学，并为卷对卷软光刻建立科学基础。为了实现这一目标，研究目标是：（1）研究接触区域的物理力学模型，用于卷对卷微接触印刷的设计和控制;（2）建立在线视觉-力-变形传感网络，用于评估印刷几何形状;（3）通过深度学习对真实的时间戳微特征图案-压力-变形行为进行建模。 印刷图案图像被系统地分割并转换成单个几何变量，该单个几何变量与用于描述印刷状态的力和位移数据同步并集成。深度学习架构通过测量打印变量和接触几何形状来对打印图案几何形状的保真度进行建模。该项目寻求以下问题的答案：（i）印刷变量和接触几何形状之间的精确可解释关系是什么;以及（ii）接触几何形状、材料特性和卷对卷印刷参数在图案几何形状形成中的作用是什么。本论文的主要目的是深入了解微接触压模的变形行为和印刷几何形状的形成机理。该项目丰富了软光刻建模、实时传感、深度学习以及卷对卷印刷工艺设计和控制的知识库，为智能制造的进步做出了贡献。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.