A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint.

García-Loureiro A, Seoane N, Fernández JG, Comesaña E, Pichel JC

DOI: 10.1371/journal.pone.0288964
Published: 2023
Container: PloS one
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0288964,
  title = {A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint.},
  author = {García-Loureiro A and Seoane N and Fernández JG and Comesaña E and Pichel JC},
  year = {2023},
  journal = {PloS one},
  doi = {10.1371/journal.pone.0288964},
  url = {https://doi.org/10.1371/journal.pone.0288964}
}

RIS

TY  - JOUR
TI  - A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint.
AU  - García-Loureiro A
AU  - Seoane N
AU  - Fernández JG
AU  - Comesaña E
AU  - Pichel JC
PY  - 2023
JO  - PloS one
DO  - 10.1371/journal.pone.0288964
UR  - https://doi.org/10.1371/journal.pone.0288964
ER  -

APA

A, G., N, S., JG, F., E, C., & JC, P. (2023). A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint.. PloS one. https://doi.org/10.1371/journal.pone.0288964

Source records

pubmed · retrieved 2026-09-25T12:17:38.329Z
europe-pmc · retrieved 2026-09-25T12:17:38.336Z