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

17 March 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Block copolymer
defect density
solvent vapor annealing
process control
directed self-assembly
machine learning
high throughput

Supplementary materials

ML+BCP - uniformity and solvent annealing SI 2021-03-16 PDF


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