The directed self-assembly of block copolymers in cylindrical holes is a promising technology for lithographic patterning, particularly in the context of vertical interconnect accesses. While the hole-shrink process for single cylinders has been extensively explored, the proliferation of morphological defects remains a significant technological barrier. We use a coarse grained model to explore morphologies that form within cylindrical confinements for combinations of template surface energies. We identify metastable defect morphologies, in addition to the desired cylindrical morphology, in majority-wetting sidewall templates. We use our coarse-grained model and the string method to identify transition pathways between defective morphologies and the cylindrical morphology to elucidate the mechanism of defect annihilation within the confinements; the transition pathway from a disordered state is also identified. This work demonstrates that the minimum free energy path for the formation of a cylinder goes through defective morphologies and that designing confinements can eliminate these undesirable transition states.

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

Directed self-assembly of block copolymers over chemically patterned substrates has proven to be an effective method for sublithographic patterning. Features on these chemical patterns can be multiplied by the natural domain-spacing of the block copolymer assembled on top of the substrate through pattern interpolation. The LiuNealey (LiNe) chemoepitaxy flow for directed self-assembly allows for modification of the geometry and chemistry of the nanopatterned substrate. The critical dimensions and period along with the chemical composition of the patterned features in the LiNe flow govern the equilibrium morphology of the assembled block copolymer. We demonstrate how the construction of the chemical pattern affects the selection for desired, well-registered assembly of block copolymer melts by using a theoretically informed coarse-grained many-body model of block copolymers. The molecular simulations are used to provide an explanation for how to best design the chemical pattern in the LiNe flow for the directed self-assembly (DSA) of block copolymers to achieve desired line-andspace structures.

Further enhancements to Monte Carlo and Self-Consistent Field Theory Directed Self-Assembly (DSA) simulation capabilities implemented in GLOBALFOUNDRIES are presented and discussed, along with the results of their applications. We present the simulation studies of DSA in graphoepitaxy confinement wells, where the DSA process parameters are varied in order to determine the optimal set of parameters resulting in a robust and etch transferrable phase morphology. A novel concept of DSA-aware assist features for the optical lithography process is presented and demonstrated in simulations. The results of the DSA simulations and studies for the DSA process using a blend of homopolymers and diblock copolymers are also presented and compared with the simulated diblock copolymer systems.

Approaches to the computational simulation of directed self-assembly (DSA) of block copolymers based on Monte-Carlo methods and self-consistent 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. (C) 2014 The Japan Society of Applied Physics

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.

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.