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.
