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

GOALI：通过多尺度在线计量监测和控制柔性电子产品的卷对卷印刷

基本信息

批准号：
1916866
负责人：
Xian Du
金额：
$ 49.88万
依托单位：
University of Massachusetts Amherst
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916866&HistoricalAwards=false
关键词：
GOALI Monitoring Control Roll Printing

项目摘要

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.

这项与行业联络的赠款机会（Goali奖）支持研究，为滚动打印过程做出了新颖的感应和控制技术，促进了革命性新的灵活电子产品的发明和制造，从而使美国在全球经济中具有竞争优势。柔性电子设备的滚动打印涉及制造薄的电子结构，沿特征大小从纳米表到毫米沿连续移动的柔性基材的特征大小，每分钟的速度不断移动。滚动打印技术提供了从根本上转移大区域纳米结构设备的成本结构的潜力，并启用了灵活功能系统的多功能应用。但是，当前连续打印过程的局限性是在线计量学不可用于过程监视和控制。这项研究为开发多尺度内计量平台的开发建立了一个技术基础。在这项研究中，沿连续移动的灵活网络的超薄印刷图案实时成像，注册和测量。该过程控制系统可以适用于不同的滚动打印过程，以适用于工业互联网和基础设施健康监测等各种应用程序。该项目涉及在具有滚动纳米制造能力的工业合作伙伴设施的学生培训学生。它将基本的研究结果纳入了本科和研究生课程，以提高学生在解决实用工程问题方面的兴趣和技能。许多实验室规模的滚动到卷到卷（R2R）打印过程已显示能够通过从纳米纳米到毫米的纳米范围的分辨率打印柔性电子设备。但是，必须将这些印刷过程扩展到工业规模，必须满足许多研究差距。研究差距包括在正常的光学成像环境中的超薄模式，模式注册的丢失，视野和分辨率的光学限制以及无法捕获R2R打印过程中的高阶动力学和非线性的常规控制方法。为了满足这些研究差距，该项目开发了纳米薄单层打印过程的印刷图案质量监控的在线计量学，研究了大型纳米纳米和微米尺度模式的高分辨率成像和注册，并探索通过对R2R打印过程进行深入学习的预测性控制过程，并通过整体式的模型和进程模型进行模型和进程模型。使用实时的水蒸气凝结图和同步图像处理对内部单层图案进行成像。预测模型是一种经常出现的条件深度预测神经网络，它结合了短期和长期非线性动态打印输入输出响应，以优化预测误差。为了解决R2R打印过程控制中涉及的广泛而复杂的问题及其对工业应用的扩展，已经建立了与Goali合作伙伴的密切合作，以指导研究工作并测试在线计量平台。该奖项反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的crietia crietia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia均值得通过评估。