Accelerating polymer self-consistent field simulation and inverse DSA-lithography with deep neural networks

Self-consistent field theory (SCFT) is a powerful polymer field-theoretic simulation tool that plays a crucial role in the study of block copolymer (BCP) self-assembly. However, the computational cost of implementing SCFT simulations is comparatively high, particularly in computationally demanding applications where repeated forward simulations are needed. Herein, we propose a deep learning-based method to accelerate the SCFT simulations. By directly mapping early SCFT results to equilibrium structures using a deep neural network (DNN), this method bypasses most of the time-consuming SCFT iterations, significantly reducing the simulation time. We first applied this method to two- and three-dimensional large-cell bulk system simulations. Both results demonstrate that a DNN can be trained to predict equilibrium states based on early iteration outputs accurately. The number of early SCFT iterations can be tailored to optimize the trade-off between computational speed and predictive accuracy. The effect of training set size on DNN performance was also examined, offering guidance on minimizing dataset generation costs. Furthermore, we applied this method to the more computationally demanding inverse directed self-assembly-lithography problem. A covariance matrix adaptation evolution strategy-based inverse design method was proposed. By replacing the forward simulation model in this method with a trained DNN, we were able to determine the guiding template shapes that direct the BCP to self-assemble into the target structure with certain constraints, eliminating the need for any SCFT simulations. This improved the inverse design efficiency by a factor of 100, and the computational cost for training the network can be easily averaged out over repeated tasks.