Block copolymer self-assembly - a computational approach towards novel morphologies

Abstract

Spontaneous self-assembly of materials is a phenomenon exhibited by different molecular systems. Among many, Block copolymers (BCPs) proved to be particularly interesting due to their ability to microphase separate into periodic domains. Nonetheless, the rising need for arbitrary, complex, 3D nanoscale morphology shows that what is commonly achievable is quite limited. Expanding the range of BCPs morphologies could be attained through the implementation of a host of strategies that could be used concurrently. Using directed self-assembly (DSA), a sphere forming BCP was assembled in a randomly displaced post template to study system resilience towards defect creation. Template shear-like distortion seemed to govern local defect generation. Defect clusters with symmetries compatible with that of the BCP showed enhanced stability.

Using 4₄ and 3₂434 Archimedean tiling templates that are incompatible with BCP six-fold symmetry created low symmetry patterns with an emergent behavior dependent on pattern size and shape. A variation of DSA is studied using modulated substrates. Layer-by-layer deposition of cylinder forming BCPs was investigated. Self-consistent field theory (SCFT) and strong segregation theory SST were employed to provide the understanding and the conditions under which particular orientations of consecutive layers were produced. Furthermore, deep functionalized trenches were employed to create vertically standing high-[chi] BCP structures. Changing annealing conditions for a self-assembled lamellar structure evolved the assembled pattern to a tubular morphology that is non-native to diblock copolymers. A rather fundamental but challenging strategy to go beyond the standard motifs common to BCPs is to synthesize multiblock molecules with an expanded design space.

Triblock copolymers produced bilayer perforated lamellar morphology. SCFT analysis showed a large window of stability of such structures in thin films. In addition, a model for bottlebrush BCPs (BBCPs) was constructed to investigate the characteristics of BBCPs self-assembly. Pre-stacked diblock sidechains showed improved microphase separation while providing domain spacing relevant to lithography applications. A rich phase diagram was constructed at different block concentrations. The ability to explore new strategies to discover potential equilibrium morphologies in BCPs is supported by strong numerical modeling and simulations efforts. Accelerating SCFT performance would greatly benefit BCP phase discovery. Preliminary work discussed the first attempt to Neural Network (NN) assisted SCFT.

The use of NN was able to cut on the required calculations steps to reach equilibrium morphology, demonstrating accelerated calculation, and escaping trapped states, with no effect on final structure.