PMID- 34100583
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210624
IS  - 1944-8252 (Electronic)
IS  - 1944-8244 (Linking)
VI  - 13
IP  - 24
DP  - 2021 Jun 23
TI  - Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of 
      Block Copolymers Using Machine Learning Approaches.
PG  - 28639-28649
LID - 10.1021/acsami.1c05056 [doi]
AB  - Self-assembly of block copolymers (BCPs) is an alternative patterning technique 
      that promises high resolution and density multiplication with lower costs. The 
      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 vapor 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. The SVA flow-controlled system enables precise 
      optimization of the conditions of self-assembly of the high Flory-Huggins 
      interaction parameter (chi) hexagonal dot-array forming BCP, 
      poly(styrene-b-dimethylsiloxane) (PS-b-PDMS). The defects within the resulting 
      patterns at various length scales are then characterized and quantified. The 
      results show that the defectivity of the resulting nanopatterned surfaces is 
      highly dependent upon very small variations of the initial film thicknesses of 
      the BCP, as well as the degree of swelling under the SVA conditions. These 
      parameters also significantly contribute to the quality of the resulting pattern 
      with respect to grain coarsening, as well as the formation of different 
      macroscale phases (single and double layers and wetting layers). The results of 
      qualitative and quantitative defect analyses are then compiled into a single 
      figure of merit (FOM) and are mapped across the experimental parameter space 
      using ML approaches, which enable the identification of the narrow region of 
      optimum conditions for SVA for a given BCP. The result of these analyses is a 
      faster and less resource intensive route toward the production of low-defectivity 
      BCP dot arrays via rational determination of the ideal combination of processing 
      factors. The DOE and machine learning-enabled approach is generalizable to the 
      scale-up of self-assembly-based nanopatterning for applications in electronic 
      microfabrication.
FAU - Ginige, Gayashani
AU  - Ginige G
AUID- ORCID: 0000-0002-4434-9648
AD  - Department of Chemistry, University of Alberta, 11227-Saskatchewan Drive, 
      Edmonton, Alberta T6G 2G2, Canada.
FAU - Song, Youngdong
AU  - Song Y
AD  - Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of 
      Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, 
      Republic of Korea.
FAU - Olsen, Brian C
AU  - Olsen BC
AUID- ORCID: 0000-0001-9758-3641
AD  - Department of Chemistry, University of Alberta, 11227-Saskatchewan Drive, 
      Edmonton, Alberta T6G 2G2, Canada.
FAU - Luber, Erik J
AU  - Luber EJ
AUID- ORCID: 0000-0003-1623-0102
AD  - Department of Chemistry, University of Alberta, 11227-Saskatchewan Drive, 
      Edmonton, Alberta T6G 2G2, Canada.
FAU - Yavuz, Cafer T
AU  - Yavuz CT
AUID- ORCID: 0000-0003-0580-3331
AD  - Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of 
      Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, 
      Republic of Korea.
AD  - KAUST Catalysis Center (KCC), Physical Sciences and Engineering (PSE), King 
      Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi 
      Arabia.
AD  - Advanced Membranes and Porous Materials Center (AMPM), Physical Sciences and 
      Engineering (PSE), King Abdullah University of Science and Technology (KAUST), 
      Thuwal 23955-6900, Saudi Arabia.
FAU - Buriak, Jillian M
AU  - Buriak JM
AUID- ORCID: 0000-0002-9567-4328
AD  - Department of Chemistry, University of Alberta, 11227-Saskatchewan Drive, 
      Edmonton, Alberta T6G 2G2, Canada.
LA  - eng
PT  - Journal Article
DEP - 20210608
PL  - United States
TA  - ACS Appl Mater Interfaces
JT  - ACS applied materials & interfaces
JID - 101504991
SB  - IM
OTO - NOTNLM
OT  - block copolymer
OT  - defect density
OT  - directed self-assembly
OT  - high throughput
OT  - machine learning
OT  - memory
OT  - process control
OT  - self-assembly
OT  - solvent vapor annealing
EDAT- 2021/06/09 06:00
MHDA- 2021/06/09 06:01
CRDT- 2021/06/08 12:15
PHST- 2021/06/09 06:00 [pubmed]
PHST- 2021/06/09 06:01 [medline]
PHST- 2021/06/08 12:15 [entrez]
AID - 10.1021/acsami.1c05056 [doi]
PST - ppublish
SO  - ACS Appl Mater Interfaces. 2021 Jun 23;13(24):28639-28649. doi: 
      10.1021/acsami.1c05056. Epub 2021 Jun 8.