GOALI: Monitoring and Control of Roll-to-Roll Printing of Flexible Electronics through Multiscale In-Line Metrology

这一学术与工业联系机会(GOALI)奖支持为卷到卷印刷过程贡献新型传感和控制技术的研究，促进革命性的新型柔性电子产品的发明和制造，使美国在全球经济中具有竞争优势。柔性电子产品的卷对卷印刷涉及沿着以每分钟几米的速度连续移动的柔性基板制造特征尺寸从纳米到毫米的薄电子结构。卷对卷打印技术提供了从根本上改变大面积纳米结构设备的成本结构的潜力，并使灵活功能系统的多种应用成为可能。然而，目前连续印刷工艺的局限性在于，在线计量不能用于工艺监测和控制。本研究为多尺度在线计量平台的开发奠定了技术基础。在这项研究中，沿着连续移动的柔性卷筒纸的超薄印刷图案被实时成像、配准和测量。该过程控制系统可适用于不同的卷对卷打印过程，适用于各种应用，如工业物联网和基础设施健康监测。该项目涉及在具有卷到卷纳米制造能力的工业合作伙伴设施中培训学生。它将基础研究成果融入本科生和研究生课程，以提高学生解决实际工程问题的兴趣和技能。许多实验室规模的卷到卷(R2R)打印工艺已被证明具有打印分辨率从纳米到毫米的柔性电子产品的能力。然而，要将这些印刷工艺扩大到工业规模，还必须填补许多研究空白。研究的差距包括超薄图案在正常光学成像环境中的不可见性、图案配准的丢失、视场和分辨率的光学限制以及传统控制方法无法捕捉R2R打印过程中的高阶动力学和非线性。为了弥补这些研究空白，本项目开发了用于纳米单层印刷工艺的印刷图案质量监测的在线计量，研究了大面积纳米和微米级图案的高分辨率成像和配准，并通过集成在线多尺度计量和过程建模来探索基于深度学习的R2R印刷过程的预测控制。使用实时水蒸气冷凝图形和同步图像处理来成像直列式单层图案。预测模型是一种递归条件深度预测神经网络，它结合了短期和长期的非线性动态印刷输入-输出响应，以优化预测误差。为了解决R2R印刷过程控制及其扩大到工业应用所涉及的广泛而复杂的一系列问题，已与GOALI合作伙伴建立了密切合作，以指导研究工作并测试在线计量平台。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.