(356b) Evolutionary Optimization of Directed Self-Assembly on Chemically Patterned Substrate

Qin, J. - Presenter, University of Chicago
Xiong, S., University of Chicago
Wan, L., HGST, a Western Digital Company
Ruiz, R., HGST, a Western Digital Company
Nealey, P. F., Argonne National Lab
de Pablo, J. J., University of Wisconsin-Madison

Directed self-assembly (DSA) of block copolymers on chemical patterns with density-multiplication is of considerable interest for sub-lithographic patterning. One of the central challenges for DSA is to optimize pattern design to achieve ideal, nearly defect-free patterns that meet industrial demands. We have addressed this problem by combining 3-D simulations of molecular assembling processes with on-the-fly optimization based on state-of-the-art evolutionary algorithms. We demonstrate the principle by optimizing the width and selectivity of chemically patterned substrates used to guide multiblock copolymers to assemble and to achieve both 3X and 4X density multiplication. We also explored the processing window by studying the effects of polymer composition, block fraction, and molecular architecture. The results compare favorably with experiments. The coupled simulation-evolution method provides a general and efficient framework for the design of complex device-oriented structures.