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

职业：通过深度学习和实时传感对卷对卷软光刻印刷过程进行建模

基本信息

批准号：
1942185
负责人：
Xian Du
金额：
$ 57.17万
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：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942185&HistoricalAwards=false
关键词：
CAREER Modeling Roll Soft Lithography

项目摘要

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.

这项教师早期职业发展（职业）赠款通过建立一种基于学习的建模方法来指导连续的微接合印刷过程的设计和控制，并研究连续的模式形成机制，从而着重于卷到卷软光刻的进步。 MicroContact软光刻是一种具有诱人的成本效益方法，它通过使用邮票在柔性基板上的选择性机械接触微接触微型和纳米尺度的特征仪表仪表方形面积。将微接合印刷调整为连续的卷到滚动平台，有助于弹性电子和可穿戴设备等应用。软光刻中传输模式的保真度取决于在邮票 - 底物界面上成功的机械接触和材料转移的控制。但是，尚未完全理解卷到滚动微接合印刷中常见结构中的基本微效力演变，无法指导打印过程的成功设计和控制。该项目研究了一种新型的建模方法，用于从模式过程中实时学习，同时将模式形成机制与打印过程变化和质量控制缺陷联系起来，以实时学习。这项研究的补充是通过多学科课程的发展与卷到滚动印刷的研究以及创建独立的动手教育套件的创建，以鼓励各种教育水平的年轻学生从事制造业的职业。该研究的目的是通过深度学习和建立科学的rollith rollith的基本机制，以了解这项研究的基础。为了实现这一目标，研究目标是：（1）研究与卷到滚动微接合印刷的设计和控制的接触区域的物理力学模型；（2）建立一个用于评估印刷几何形状的内线视觉传感网络；（3）通过深度学习模型实时邮票微摄影模式形式形式的行为。印刷图像图像是系统分割的，并将其转换为单个几何变量，该变量已同步并与用于描述印刷状态的力和位移数据集成并集成。深度学习架构通过测量打印变量和接触几何形状来建模打印模式几何形状的忠诚度。该项目寻求以下问题的答案：（i）打印变量与接触几何形状之间的确切可解释关系是什么；（ii）在形成模式几何形状中，接触几何，材料特性和卷到滚动打印参数的作用是什么？总体重点是深入了解微接触邮票的变形行为和印刷几何形状的形成机理。该项目丰富了软性光刻建模，实时感测，深度学习以及对滚动打印过程的设计和控制的知识库，并为智能制造的进步做出了贡献。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和广泛影响的评估来通过评估来获得支持的。