Litcius/Paper detail

Understanding Kinetics of Defect Annihilation in Chemoepitaxy-Directed Self-Assembly

Jiajing Li, Paulina A. Rincon-Delgadillo, Hyo Seon Suh, G. Mannaert, Paul F. Nealey

2021ACS Applied Materials & Interfaces11 citationsDOI

Abstract

Directed self-assembly (DSA) of block copolymers (BCP) has attracted considerable interest from the semiconductor industry because it can achieve semiconductor-relevant structures with a relatively simple process and low cost. However, the self-assembling structures can become kinetically trapped into defective states, which greatly impedes the implementation of DSA in high-volume manufacturing. Understanding the kinetics of defect annihilation is crucial to optimizing the process and eventually eliminating defects in DSA. Such kinetic experiments, however, are not commonly available in academic laboratories. To address this challenge, we perform a kinetic study of chemoepitaxy DSA in a 300 mm wafer fab, where the complete defectivity information at various annealing conditions can be readily captured. Through extensive statistical analysis, we reveal the statistical model of defect annihilation in DSA for the first time. The annihilation kinetics can be well described by a power law model, indicating that all dislocations can be removed by sufficiently long annealing time. We further develop image analysis algorithms to analyze the distribution of dislocation size and configurations and discover that the distribution stays relatively constant over time. The defect distribution is determined by the role of the guiding stripe, which is found to stabilize the defects. Although this study is based on polystyrene-b-poly(methyl methacrylate) (PS-b-PMMA), we anticipate that these findings can be readily applied to other BCP platforms as well.

Topics & Concepts

AnnihilationMaterials scienceWaferAnnealing (glass)KineticsSemiconductorKinetic energyNanotechnologyOptoelectronicsComposite materialNuclear physicsPhysicsQuantum mechanicsBlock Copolymer Self-AssemblyAdvancements in Photolithography TechniquesMachine Learning in Materials Science