SNM: Inverse Design of Nanostructured Heterogeneous Materials

SNM：纳米结构异质材料的逆向设计

• 批准号: 1246740
• 负责人: Alfredo Alexander-Katz
• 金额: $ 120万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1246740&HistoricalAwards=false
• 关键词: SNM; Inverse; Design; Nanostructured; Heterogeneous

This Scalable NanoManufacturing (SNM) grant provides funding for the development of a computational platform that will enable the synthesis of heterogeneous hierarchical nanomaterials in a prescribed and efficient manner. Such materials will be formed using block copolymer self-assembly on graphoepitaxial templates, a process known as templated self-assembly. The developed numerical tool will solve the inverse problem for the self-assembly process in which the input is the final nanoscale pattern (the target), and the output is the optimal template configuration that will yield such pattern given a set of constraints. The developed inverse self-assembly algorithm will be implemented in advanced parallel computational architectures such as graphic processing units. The algorithm will be tested in three different cases: periodic patterns with non-trivial symmetries, precise arrangement of nanoparticles in block copolymer matrices, and the interconnection of patterns with different symmetries. Experiments for each of the aforementioned cases will be performed to validate the computational results. If successful, this research will lead to novel algorithms in solving the inverse self-assembly problem that in turn will lead to advances in the manufacturing of heterogeneous nanoscale materials. The primary goal of this project is to develop a toolkit that will determine the optimal templates for a given target pattern. Solving such problem on the computer in a directed and optimal fashion will reduce the costs and time to empirically construct the templates used in templated self-assembly. This has the potential to reduce the overall costs of nanomanufacturing by accelerating the processes and increasing the fidelity of the patterns for lithographic applications such as those needed in the semiconductor industry.

这项可伸缩纳米制造(SNM)赠款为计算平台的开发提供资金，该平台将能够以规定和有效的方式合成不同种类的分级纳米材料。这种材料将利用嵌段共聚物在梯度外延模板上的自组装形成，这一过程被称为模板自组装。所开发的数值工具将解决自组装过程的逆问题，其中输入是最终的纳米级图案(目标)，输出是在给定一组约束的情况下产生此类图案的最佳模板配置。开发的逆自组装算法将在高级并行计算体系结构中实现，如图形处理单元。该算法将在三种不同的情况下进行测试：具有非平凡对称性的周期性图案，纳米粒子在嵌段共聚矩阵中的精确排列，以及不同对称性图案的互连。将对上述每种情况进行实验，以验证计算结果。如果成功，这项研究将带来解决反向自组装问题的新算法，这反过来将导致异质纳米材料制造的进步。这个项目的主要目标是开发一个工具包，它将为给定的目标模式确定最佳模板。在计算机上以定向和优化的方式解决这样的问题将减少经验地构建用于模板化自组装的模板的成本和时间。这有可能通过加速工艺和提高用于光刻应用的图案的保真度来降低纳米制造的总体成本，例如半导体行业所需的那些应用。