FMRG: Cyber: Manufacturing USA: Cyber-Enabled, High-Throughput Manufacturing of Multi-Material, 3D Nanostructures

高速、纳米级3D打印有可能改变制造业，使许多目前无法生产的产品成为可能。不幸的是，当代3D打印技术远远达不到许多潜在应用的吞吐量、分辨率和产量要求。这笔拨款将支持研究开发一种新型纳米级3D打印技术，这将彻底改变我们制造产品的能力，如需要精确的多材料3D纳米结构的水过滤膜。与最先进的微/纳米级3D打印相比，我们期望这项工作通过允许同时用多种材料制造体积纳米结构，将空间分辨率提高一个数量级以上，吞吐量提高五个数量级。通过这种纳米级3D打印工艺实现的新型水过滤膜将提高水过滤的选择性和渗透性之间的平衡，估计可提高10倍，同时将膜成本降低100倍。这些改进将使超滤的总成本降低高达25%，每年可能节省数十亿美元。此计划亦会透过以下方式促进劳动力发展：(1)创建一个新的纳米工程证书项目，以快速培训半导体行业的工人；(2)开发一个新的纳米工程硕士项目，针对代表性不足的群体的学生；(3)利用教师研究经验（RET）项目，将“学习实验室”带到代表性不足的少数民族人口较多的学校；(4)与工业合作伙伴合作，为学生创造实习机会。在传统的纳米级3D打印方法中，分辨率、精度和吞吐量的提高经常发生冲突。在此资助下开发的纳米级3D打印工艺将通过使用亚波长图案的元掩膜在新的多波长光固化抗蚀剂中创建近场多色全息图案来结束这些权衡，从而允许在单次光照下以亚衍射分辨率对整个多材料3D结构进行图案。网络数据分析将用于为单个子工艺和整体掩模和材料设计创建反馈回路。使用新型计量工具对这些3D纳米结构进行智能采样将与机器学习和基于物理的模型相结合，以创建一个混合框架，以测试纳米级3D打印的基本限制。这项工作的预期成果包括：(1)创建用于多波长3D纳米图形的近场超掩模的新方法，(2)实现多波长、多材料图形的新抗蚀剂化学，(3)对纳米级3D打印中限制分辨率、吞吐量、材料特性和产量的物理的新理解，(4)用于实时测量纳米级3D特征的高速、数据支持混合计量新方法，(5)利用计量数据和基于物理的模型智能提高产量的新型混合控制技术。这项未来制造研究由土木、机械和制造创新部门（ENG/CMMI）、化学部门（MPS/CHE）和工程教育和中心（ENG/EEC）支持。该奖项反映了美国国家科学基金会的法定使命，并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。

High-speed, nanoscale 3D printing has the potential to transform manufacturing and enable the fabrication of many products that are currently infeasible to produce. Unfortunately, contemporary 3D printing techniques fall well short of the throughput, resolution, and yield requirements of many potential applications. This grant will support research to develop a novel nanoscale 3D printing technique that will revolutionize our ability to manufacture products such as water filtration membranes that require precise, multi-material 3D nanostructures. Compared with state-of-the-art micro/nanoscale 3D printing, we expect this work to increase the spatial resolution by over an order of magnitude and the throughput by five orders of magnitude by allowing volumetric nanostructures to be fabricated from multiple materials simultaneously. The new water filtration membranes enabled by this nanoscale 3D printing process will improve the tradeoff between selectivity and permeability in water filtration by an estimated factor of 10x while reducing membrane costs by a factor of 100x. These improvements will result in an overall cost reduction in ultrafiltration of up to 25%, potentially save billions of dollars per year. This project will also enhance workforce development by: (1) creating a new NanoEngineering certificate program to rapidly train workers for the semiconductor industry, (2) developing a new NanoEngineering Master’s program targeting students from underrepresented groups, (3) leveraging the Research Experiences for Teachers (RET) program to bring the “learning labs” to schools with large underrepresented-minority populations, and (4) working with industrial partners to create internships opportunities for students.In traditional approaches to nanoscale 3D printing, improvements in resolution, precision, and throughput often conflict. The nanoscale 3D printing process developed under this grant will end these tradeoffs by using sub-wavelength-patterned metamasks to create near-field multi-colored holographic patterns in new multi-wavelength photocurable resists to allow entire multi-material 3D structures to be patterned with sub-diffraction resolution in a single light exposure. Cyber-data analytics will be used to create feedback loops for both individual sub-processes and the overall mask and materials designs. Smart sampling of these 3D nanostructures using novel metrology tools will be combined with machine learning and physics-based models to create a hybrid framework to test the fundamental limits of nanoscale 3D printing. Expected outcomes of this work include: (1) new methods for creating near-field metamasks for multi-wavelength 3D nanopatterning, (2) new resist chemistries enabling multi-wavelength, multi-material patterning, (3) new understanding of the physics that limit resolution, throughput, material properties, and yield in nanoscale 3D printing, (4) new high-speed, data-enabled hybrid metrology approaches for measuring nanoscale 3D features in real time, and (5) new hybrid control techniques that use metrology data and physics-based models to intelligently enhance yield.This Future Manufacturing research is supported by the Divisions of Civil, Mechanical and Manufacturing Innovation (ENG/CMMI), Chemistry (MPS/CHE), and Engineering Education and Centers (ENG/EEC).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.