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Machine Learning Predictions of Block Copolymer Self‐Assembly
Author(s) -
Tu KunHua,
Huang Hejin,
Lee Sangho,
Lee Wonmoo,
Sun Zehao,
AlexanderKatz Alfredo,
Ross Caroline A.
Publication year - 2020
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.202005713
Subject(s) - nanomanufacturing , materials science , copolymer , nanolithography , simulated annealing , lithography , photolithography , annealing (glass) , nanotechnology , dewetting , computer science , artificial intelligence , biological system , polymer , machine learning , thin film , composite material , optoelectronics , fabrication , medicine , alternative medicine , pathology , biology
Directed self‐assembly of block copolymers is a key enabler for nanofabrication of devices with sub‐10 nm feature sizes, allowing patterning far below the resolution limit of conventional photolithography. Among all the process steps involved in block copolymer self‐assembly, solvent annealing plays a dominant role in determining the film morphology and pattern quality, yet the interplay of the multiple parameters during solvent annealing, including the initial thickness, swelling, time, and solvent ratio, makes it difficult to predict and control the resultant self‐assembled pattern. Here, machine learning tools are applied to analyze the solvent annealing process and predict the effect of process parameters on morphology and defectivity. Two neural networks are constructed and trained, yielding accurate prediction of the final morphology in agreement with experimental data. A ridge regression model is constructed to identify the critical parameters that determine the quality of line/space patterns. These results illustrate the potential of machine learning to inform nanomanufacturing processes.