SNM: Inverse Design of Nanostructured Heterogeneous Materials

这项可扩展纳米制造（SNM）拨款为开发计算平台提供资金，该平台将能够以规定的有效方式合成异质分层纳米材料。这种材料将在石墨外延模板上使用嵌段共聚物自组装形成，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反问题，其中输入是最终的纳米尺度图案（目标），输出是给定一组约束条件下产生该图案的最佳模板配置。所开发的逆自组装算法将在图形处理单元等先进的并行计算架构中实现。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，嵌段共聚物矩阵中纳米颗粒的精确排列，以及具有不同对称性的图案的互连。将对上述每种情况进行实验以验证计算结果。如果成功，这项研究将导致解决逆自组装问题的新算法，进而将导致非均质纳米级材料制造的进步。这个项目的主要目标是开发一个工具包，该工具包将为给定的目标模式确定最佳模板。在计算机上以定向和最优的方式解决这类问题，将减少经验构造模板自组装所用模板的成本和时间。这有可能通过加速工艺和提高光刻应用（如半导体工业中所需的光刻应用）的图案保真度来降低纳米制造的总体成本。

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.