BRITE Pivot: Machine Learning Enabled Rapid and Robust Three-Dimensional Nanomanufacturing

BRITE Pivot：机器学习实现快速、稳健的三维纳米制造

• 批准号: 2135585
• 负责人: Xianfan Xu
• 金额: $ 45.96万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2135585&HistoricalAwards=false
• 关键词: BRITE; Pivot; Machine; Learning; Enabled

Three-dimensional (3D) printing is one of the most important manufacturing technology developments in recent years for applications ranging from prototyping and product visualization to building functional materials and devices. Nanoscale 3D printing has been used to produce a wide range of complex 3D nanostructures and nanodevices with unprecedented properties and functionalities. However, the slow speed, variable quality and poor reproducibility of current 3D nanoprinting methods, such as point-by-point laser printing, are barriers for their adoption to commercial-scale manufacturing. Machine learning (ML) and artificial intelligence (AI) are ideally suited for improving and assuring the quality of the printed structures. This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Pivot award develops AI-guided, ML-enabled 3D nanoprinting methods to improve the speed, scale, print quality and robustness of the printed structures and devices. These AI and ML tools for 3D nanomanufacturing enable new applications, such as sensors and wearables, that benefit several sectors of the economy and contribute to US competitiveness and global leadership in advanced nanomanufacturing. This project contributes to diversity, equity and inclusion by recruiting students from underrepresented groups and engaging them in research and education in AI, ML and 3D nanoprinting technologies, thus developing a diverse workforce in advanced manufacturing. The PI has extensive experience and expertise in laser-based nanomanufacturing and device manufacture. Recent efforts by the principal investigator (PI)’s group have resulted in the development of a rapid, continuous, layer-by-layer 3D nanoprinting technology. This project allows the PI to acquire new expertise in ML and AI methods through in-depth investigations and applications of these methods to develop rapid and robust 3D nanoprinting technologies. This includes studies and evaluations of various ML and AI methods and selection and implementation of appropriate methods for rapid 3D nanoprinting. The research involves developing methods that combine physics-based models with advanced ML algorithms such as adaptive learning and transfer learning. The femtosecond laser-based 3D nanoprinting process involves understanding the fundamentals of controlling the laser beam characteristics, such as beam size and scan rate, its propagation in the optical system and the laser-induced reactions in the photopolymers. The physics-based models and laser photo-polymerization experiments provide key data and guidance for the development of ML and AI analytic tools for process control and optimization. Furthermore, the project studies approaches to develop ML and AI tools using a relatively small amount of experimental data. Using results of various printed structures, ML and AL algorithms are developed to guide the process of printing arbitrary, user-defined structures with high precision. This project advances knowledge in AI-guided, ML-enabled femtosecond laser-based photo-polymerization as a platform for rapid and robust 3D nanomanufacturing.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.

三维(3D)打印是近年来最重要的制造技术发展之一，其应用范围从原型制作和产品可视化到建筑功能材料和设备。纳米级3D打印已经被用来制造具有前所未有的特性和功能的各种复杂的3D纳米结构和纳米器件。然而，目前的3D纳米打印方法，如逐点激光打印，速度慢，质量不稳定，重复性差，这是它们应用于商业规模生产的障碍。机器学习(ML)和人工智能(AI)非常适合于改进和确保印刷结构的质量。这一促进工程转型和公平进步的研究想法(BRITE)Pivot奖开发了人工智能引导的、支持ML的3D纳米打印方法，以提高打印结构和设备的速度、规模、打印质量和稳健性。这些用于3D纳米制造的AI和ML工具使传感器和可穿戴设备等新应用得以实现，这些应用使几个经济部门受益，并有助于美国在先进纳米制造领域的竞争力和全球领先地位。该项目通过从代表性不足的群体中招收学生，并让他们参与人工智能、ML和3D纳米打印技术的研究和教育，从而在先进制造业中培养多样化的劳动力，从而促进多样性、公平和包容性。PI在基于激光的纳米制造和设备制造方面拥有丰富的经验和专业知识。首席研究员(PI)的S团队最近的努力导致了快速、连续、逐层3D纳米打印技术的发展。该项目使PI能够通过深入研究和应用这些方法来获得ML和AI方法方面的新专业知识，以开发快速和强大的3D纳米打印技术。这包括对各种ML和AI方法的研究和评估，以及选择和实施适用于快速3D纳米打印的方法。这项研究涉及开发将基于物理的模型与先进的ML算法相结合的方法，如自适应学习和转移学习。基于飞秒激光的三维纳米打印过程包括了解控制激光特性的基本原理，如光束大小和扫描速度、激光在光学系统中的传播以及光聚合物中的激光诱导反应。基于物理的模型和激光光聚合实验为ML和AI分析工具的开发提供了关键数据和指导，用于过程控制和优化。此外，该项目研究了使用相对少量的实验数据开发ML和AI工具的方法。利用各种打印结构的结果，开发了ML和AL算法，以指导高精度打印任意、用户定义的结构的过程。该项目促进了人工智能引导的、基于ML的基于飞秒激光的光聚合方面的知识，作为快速和强大的3D纳米制造的平台。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。

项目成果

The physics of 3D printing with light

• DOI: 10.1038/s42254-023-00671-3
• 发表时间: 2023-12
• 期刊: Nature Reviews Physics
• 影响因子: 38.5
• 作者: P. Somers;Alexander Münchinger;Shoji Maruo;Christophe Moser;X. Xu;M. Wegener

Photo-activated polymerization inhibition process in photoinitiator systems for high-throughput 3D nanoprinting

高通量 3D 纳米打印光引发剂系统中的光激活聚合抑制过程

• DOI: 10.1515/nanoph-2022-0611
• 发表时间: 2023
• 期刊: Nanophotonics
• 影响因子: 7.5
• 作者: Somers, Paul;Liang, Zihao;Chi, Teng;Johnson, Jason E.;Pan, Liang;Boudouris, Bryan W.;Xu, Xianfan
• 通讯作者: Xu, Xianfan

Model for polymerization and self-deactivation in two-photon nanolithography

• DOI: 10.1364/oe.461969
• 发表时间: 2022-07-18
• 期刊: OPTICS EXPRESS
• 影响因子: 3.8
• 作者: Johnson, Jason E.;Chen, Yijie;Xu, Xianfan
• 通讯作者: Xu, Xianfan