Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-assembly of Block Copolymers Using Machine Learning Approaches

Self-assembly of block copolymers (BCP) is an alternative patterning technique that promises sublithographic resolution and density multiplication. Defectivity of the resulting nanopatterns remains too high for many applications in microelectronics, and is exacerbated by small variations of processing parameters, such as film thickness, and fluctuations of solvent vapour pressure and temperature, among others. In this work, a solvent vapor annealing (SVA) flow-controlled system is combined with Design of Experiments (DOE) and machine learning (ML) approaches.