Evolutionary Optimization of Directed Self-Assembly of Triblock Copolymers on Chemically Patterned Substrates

Area of Science:

• Materials Science
• Nanotechnology
• Computational Modeling

Background:

• Directed self-assembly of block copolymers is crucial for sublithographic patterning.
• Pattern interpolation multiplies substrate features using block copolymer morphology for small, defect-free patterns.
• A key challenge is identifying conditions for ideal, defect-free directed assembly.

Purpose of the Study:

• To develop and demonstrate a computational approach for designing substrate-copolymer combinations for lithographic patterning.
• To optimize directed self-assembly processes for defect-free feature fabrication.
• To provide an efficient method for designing complex device structures.

Main Methods:

• Utilized a coarse-grained many-body model of block copolymers.
• Coupled the model with an evolutionary computation (EC) strategy.
• Employed simulation-evolution methods for design and optimization.

Main Results:

• The proposed approach significantly outperforms traditional random search algorithms.
• Successfully designed and optimized substrate-copolymer systems for defect-free directed assembly.
• Validated simulation results against recent experimental observations.

Conclusions:

• The coupled simulation-evolution method offers a general and efficient strategy for designing directed self-assembly processes.
• This approach facilitates the fabrication of extremely small, defect-free features over large areas.
• Enables potential design of complex device-oriented nanostructures.