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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202005713
|2 doi
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|a pubmed24n1059.xml
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Tu, Kun-Hua
|e verfasserin
|4 aut
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| 245 |
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|a Machine Learning Predictions of Block Copolymer Self-Assembly
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|c 2020
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Revised 29.12.2020
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2020 Wiley-VCH GmbH.
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|a 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
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|a Journal Article
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|a block copolymers
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| 650 |
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|a machine learning
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4 |
|a nanomanufacturing
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| 650 |
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4 |
|a ridge regression
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| 650 |
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4 |
|a self-assembly
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| 700 |
1 |
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|a Huang, Hejin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lee, Sangho
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lee, Wonmoo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sun, Zehao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Alexander-Katz, Alfredo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ross, Caroline A
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 32(2020), 52 vom: 23. Dez., Seite e2005713
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
1 |
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|g volume:32
|g year:2020
|g number:52
|g day:23
|g month:12
|g pages:e2005713
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| 856 |
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|u http://dx.doi.org/10.1002/adma.202005713
|3 Volltext
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