A non-parametric shape optimization approach for solving inverse problems in directed self-assembly of block copolymers

TL;DR

This paper introduces a non-parametric shape optimization method to design guiding patterns that achieve specific self-assembly morphologies in block copolymer systems, using sensitivity analysis within a theoretical framework.

Contribution

It develops a novel non-parametric approach for optimizing guiding pattern shapes in block copolymer self-assembly, enhancing control over the resulting morphologies.

Findings

The method effectively minimizes morphology misfit in simulations.

The approach demonstrates robustness across different pattern shapes.

Applications include templating for vertical interconnects.

Abstract

In this work we consider the inverse problem of finding guiding pattern shapes that result in desired self-assembly morphologies of block copolymer melts. Specifically, we model polymer self-assembly using Self-Consistent Field Theory and derive in a non-parametric setting the sensitivity of the misfit between desired and actual morphologies to arbitrary perturbations in the guiding pattern shape. The obtained sensitivities are used for optimization of the confining pattern shapes such that the misfit between desired and actual morphologies is minimized. The efficiency and robustness of the proposed algorithm is demonstrated on a number of examples related to templating Vertical Interconnect Accesses.