Simulation study of the effect of molar mass dispersity on domain interfacial roughness in lamellae forming block copolymers for directed self-assembly

Template Design for Complex Block Copolymer Patterns Using a Machine Learning Method

This study represents the first attempt to address the inverse design problem of the guiding template for directed self-assembly (DSA) patterns using solely machine learning methods. By formulating the problem as a multi-label classification task, the study shows that it is possible to predict templates without requiring any forward simulations. A series of neural network (NN) models, ranging from the basic two-layer convolutional neural network (CNN) to the large NN models (32-layer CNN with 8 residual blocks), have been trained using simulated pattern samples generated by thousands of self-consistent field theory (SCFT) calculations; a number of augmentation techniques, especially suitable for predicting morphologies, have been also proposed to enhance the performance of the NN model. The exact match accuracy of the model in predicting the template of simulated patterns was significantly improved from 59.8% for the baseline model to 97.1% for the best model of this study. The best model also demonstrates an excellent generalization ability in predicting the template for human-designed DSA patterns, while the simplest baseline model is ineffective in this task.

Rapid access to discrete and monodisperse block co-oligomers from sugar and terpenoid toward ultrasmall periodic nanostructures

Discrete block co-oligomers (BCOs) are gaining considerable attention due to their potential to form highly ordered ultrasmall nanostructures suitable for lithographic templates. However, laborious synthetic routes present a major hurdle to the practical application. Herein, we report a readily available discrete BCO system that is capable of forming various self-assembled nanostructures with ultrasmall periodicity. Click coupling of propargyl-functionalized sugars (containing 1-7 glucose units) and azido-functionalized terpenoids (containing 3, 4, and 9 isoprene units) afforded the discrete and monodisperse BCOs with a desired total degree of polymerization and block ratio. These BCOs microphase separated into lamellar, gyroid, and cylindrical morphologies with the domain spacing (d) of 4.2-7.5 nm. Considering easy synthesis and rich phase behavior, presented BCO systems could be highly promising for application to diverse ~4-nm nanofabrications.

Phase behavior of AB/CD diblock copolymer blends via coarse-grained simulation

The phase diagram of equimolar blends of AB and CD diblock copolymers has been studied using dissipative particle dynamics. All unlike blocks interacted with the same χ, except for the B-C interaction, for which χ_BC < 0 in order to prevent macrophase separation. The BC interaction was able to prevent macrophase separation except for low volume fractions of B and C (φ_BC⪅ 0.1) and relatively equal fractions of A and D. For high φ_BC (φ_BC⪆ 0.92), a disordered state was obtained. For all microphase separated states the shapes/morphologies were described by the ratios of the eigenvalues of the radius of gyration tensor and their sphericity. These were used to classify the domains as forming sphere, cylinders, lamellae, or branched/gyroidal structures. For φ_BC < 0.5 the BC domains acted as an interfacial region which compatibilized the A and D domains, while for φ_BC > 0.5 the BC domain filled in the space between A and D domains. Several interesting structures were formed including a novel connected/branched spheres morphology, hierarchical lamellae, concentric spheres/cylinders, and a combination of cylinders/lamellae. Comparisons are made with the linear diblock and linear triblock phase diagrams.