Directed self-assembly of block copolymers on chemical patterns is of considerable interest for sublithographic patterning. The concept of pattern interpolation, in which a subset of features patterned on a substrate is multiplied through the inherent morphology of an ordered block copolymer, has enabled fabrication of extremely small, defect-free features over large areas. One of the central challenges in design of pattern interpolation strategies is that of identifying system characteristics leading to ideal, defect-free directed assembly. In this work we demonstrate how a coarse-grained many-body model of block copolymers, coupled to an evolutionary computation (EC) strategy, can be used to design and optimize substrate–copolymer combinations for use in lithographic patterning. The proposed approach is shown to be significantly more effective than traditional algorithms based on random searches, and its results are validated in the context of recent experimental observations. The coupled simulation–evolution method introduced here provides a general and efficient method for potential design of complex device-oriented structures.

Directed assembly of block polymers is rapidly becoming a viable strategy for lithographic patterning of nanoscopic features. One of the key attributes of directed assembly is that an underlying chemical or topographic substrate pattern used to direct assembly need not exhibit a direct correspondence with the sought after block polymer morphology, and past work has largely relied on trial-and-error approaches to design appropriate patterns. In this work, a computational evolutionary strategy is proposed to solve this optimization problem. By combining the Cahn-Hilliard equation, which is used to find the equilibrium morphology, and the covariance-matrix evolutionary strategy, which is used to optimize the combined outcome of particular substrate-copolymer combinations, we arrive at an efficient method for design of substrates leading to non-trivial, desirable outcomes.

A theoretical method for developing design rules for the directed self-assembly of block copolymers for lithographic applications.

Approaches to the computational simulation of directed self-assembly (DSA) of block copolymers based on Monte-Carlo methods and selfconsistent field theory are presented and reviewed, with an emphasis on computational models of DSA processes usable for fabrication of integrated circuits (ICs). Applications of such models are illustrated by presenting the results of simulations used in the development of DSA fabrication processes. The inverse DSA problem, or DSA proximity correction (DSA PC) problem, is formulated, and the methods for its computational solution are presented. The application of one of these methods is illustrated by demonstrating co-optimization of optical proximity correction (OPC) and DSA PC for IC vias fabricated using a graphoepitaxy DSA process.