Machine Learning Predictions of Block Copolymer Self‐Assembly
- 18 November 2020
- journal article
- research article
- Published by Wiley in Advanced Materials
- Vol. 32 (52), e2005713
- https://doi.org/10.1002/adma.202005713
Abstract
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.Funding Information
- National Science Foundation (DMR1606911)
- Materials Research Science and Engineering Center, Harvard University (DMR1419807)
This publication has 72 references indexed in Scilit:
- Accelerating materials property predictions using machine learningScientific Reports, 2013
- Self-assembly of block copolymer thin filmsMaterials Today, 2010
- Reversible Morphology Control in Block Copolymer Films via Solvent Vapor Processing: An in Situ GISAXS StudyMacromolecules, 2010
- Block Copolymer Based Nanostructures: Materials, Processes, and Applications to ElectronicsChemical Reviews, 2009
- Density Multiplication and Improved Lithography by Directed Block Copolymer AssemblyScience, 2008
- Dense Self‐Assembly on Sparse Chemical Patterns: Rectifying and Multiplying Lithographic Patterns Using Block CopolymersAdvanced Materials, 2008
- Block copolymers and conventional lithographyMaterials Today, 2006
- Novel Inorganic−Organic Hybrid Block Copolymers as Pore Generators for Nanoporous Ultralow-Dielectric-Constant FilmsMacromolecules, 2005
- Block Copolymer Thermodynamics: Theory and ExperimentAnnual Review of Physical Chemistry, 1990
- Ridge Regression: Biased Estimation for Nonorthogonal ProblemsTechnometrics, 1970