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Fully Automated Inverse Co-Optimization of Templates and Block Copolymer Blending Recipes for Directed Self-Assembly

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Directed self-assembly (DSA) using block copolymers (BCPs) fabricates sub-10 nm nanoholes. A Gaussian descriptor and Bayesian optimization efficiently design templates for precise BCP self-assembly and manufacturability.

Keywords:
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Area of Science:

  • Materials Science
  • Nanotechnology
  • Chemical Engineering

Background:

  • Directed self-assembly (DSA) of block copolymers (BCPs) is crucial for fabricating nanoscale features like contact holes.
  • Precisely controlling nanoholes size and position requires template guidance, but template shape optimization is challenging.
  • Ensuring manufacturability of optimized templates is vital for practical applications.

Purpose of the Study:

  • To develop an efficient method for parametrizing and optimizing template shapes for BCP directed self-assembly.
  • To improve the adaptability of BCP systems to template designs.
  • To ensure the manufacturability of optimized templates for sub-10 nm technology nodes.

Main Methods:

  • A Gaussian descriptor was proposed to characterize template shapes using minimal parameters.
  • AB/AB binary blends were used instead of pure diblock copolymers for enhanced adaptability.
  • Bayesian optimization (BO) was employed to co-optimize the binary blend composition and template shape.
  • Constraints on template curvature variation were imposed to ensure manufacturability.

Main Results:

  • The Gaussian descriptor combined with BO efficiently identified optimal templates for various multihole patterns.
  • Achieved highly matched self-assembled morphologies with precisely controlled nanoholes.
  • Optimized templates demonstrated superior manufacturability due to curvature constraints.
  • Key blend parameters showed wide tunable windows for high-precision fabrication.

Conclusions:

  • The proposed Gaussian descriptor and BO framework offer an efficient approach for designing templates in BCP DSA.
  • This method significantly advances the fabrication of nanoscale features for semiconductor manufacturing.
  • The findings provide valuable insights for the practical application of DSA technology.