SNM: Robust Scalable Nanomanufacturing of Photonic Structures

SNM：稳健可扩展的光子结构纳米制造

• 批准号: 1530734
• 负责人: Cheng Sun
• 金额: $ 146.72万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1530734&HistoricalAwards=false
• 关键词: SNM; Robust; Scalable; Nanomanufacturing; Photonic

This project addresses the fundamental challenges in developing the low-cost and highly scalable nanomanufacturing solutions. The mainstream nanofabrication technologies often require the precise placement of materials at nanometer scale accuracy and thus, can be rather slow and expensive. On the other hand, self-assembly process of nanoparticles allows for spontaneous formation of large-scale nanostructures into certain patterns of configurations to achieve desired functions at very fast speed and low cost. In fact, this is the strategy being adopted by the nature in creating the fascinating structural coloration in many living creatures, such as one found in the birds' feathers. Taking the inspiration from the Nature, we will develop a new design methodology in enabling scalable nanomanufacturing process using the low-cost self-assembly process while simultaneously develop the strategy to control and mitigate the manufacturing defects. The success of this project will improve the robustness of scalable nanomanufacturing process and products and therefore accelerate the transformation from nanotechnology to a broad range of profitable commercial products. The emphasis on the solar energy will have a notable positive impact on developing green, alternative energy suppliers. This project will also establish a wide range of dissemination and outreach programs, with particular focus on the new hands-on teaching module to attract the attention of students from underrepresented groups to engineering careers and research opportunities. The intellectual significance of this project to develop a novel robust scalable nanomanufacturing methodology that integrates: 1) non-deterministic design representations that exploit the stochastic nature of bottom-up nanomanufacturing processes; 2) a manufacturing-aware physical modeling strategy that facilitates a direct process-structure-performance data pipeline; 3) a concurrent and robust design framework that allows simultaneous optimization of the structure and process variables; and ultimately 4) nanometrology assisted defect detection and control that combines statistical process control, stochastic model correction, and robust design for a complete suite of cost-effective and robust scalable nanomanufacturing solutions. The methodology will be validated using two testbeds: a) low-cost self-assembled block-copolymer light-trapping coating for thin film solar cells, and b) smart-window coating through hierarchical assembly of nanocomposite materials. A wide range of dissemination and outreach programs will be established, with particular focus on the new hands-on teaching module to attract the attention of students from under-represented groups to engineering careers and research opportunities. The work offers unique research and educational experiences for researchers across the fields of design, nano-engineering, and industrial statistics, and will train students in an interdisciplinary learning environment.

该项目解决了开发低成本和高度可扩展的纳米制造解决方案的基本挑战。主流的纳米纤维技术通常需要以纳米级精度精确放置材料，因此可能相当缓慢和昂贵。另一方面，纳米颗粒的自组装过程允许自发形成大规模的纳米结构成一定的构型图案，以非常快的速度和低成本实现期望的功能。事实上，这是大自然在许多生物中创造迷人的结构色彩时所采用的策略，例如在鸟类的羽毛中发现的一种。从大自然中获得灵感，我们将开发一种新的设计方法，使用低成本的自组装过程实现可扩展的纳米制造过程，同时开发控制和减轻制造缺陷的策略。该项目的成功将提高可扩展的纳米制造工艺和产品的稳健性，从而加速从纳米技术到广泛的有利可图的商业产品的转变。 对太阳能的重视将对发展绿色替代能源供应商产生显著的积极影响。该项目还将建立广泛的传播和外展计划，特别关注新的实践教学模块，以吸引来自代表性不足群体的学生对工程职业和研究机会的关注。该项目的智力意义是开发一种新的鲁棒可扩展的纳米制造方法，该方法集成：1）利用自底向上纳米制造过程的随机性的非确定性设计表示; 2）促进直接工艺-结构-性能数据管道的制造感知物理建模策略; 3）一个并行和鲁棒的设计框架，允许同时优化结构和过程变量;以及最终4）结合统计过程控制，随机模型校正，和稳健的设计，提供一整套具有成本效益和稳健的可扩展纳米制造解决方案。该方法将使用两个测试平台进行验证：a）用于薄膜太阳能电池的低成本自组装嵌段共聚物光捕获涂层，以及B）通过纳米复合材料的分层组装的智能窗涂层。将建立广泛的传播和推广计划，特别注重新的实践教学模块，以吸引来自代表性不足群体的学生对工程职业和研究机会的关注。这项工作为设计，纳米工程和工业统计领域的研究人员提供了独特的研究和教育经验，并将在跨学科的学习环境中培养学生。

项目成果

Designing anisotropic microstructures with spectral density function

• DOI: 10.1016/j.commatsci.2020.109559
• 发表时间: 2019-08
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Akshay Iyer;Rabindra Dulal;Yichi Zhang;Umar Farooq Ghumman;T. Chien;G. Balasubramanian;Wei Chen