In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 7 ( 2023-7-24), p. e0288964-
Abstract:
The performance and reliability of semiconductor devices scaled down to the sub-nanometer regime are being seriously affected by process-induced variability. To properly assess the impact of the different sources of fluctuations, such as line edge roughness (LER), statistical analyses involving large samples of device configurations are needed. The computational cost of such studies can be very high if 3D advanced simulation tools (TCAD) that include quantum effects are used. In this work, we present a machine learning approach to model the impact of LER on two gate-all-around nanowire FETs that is able to dramatically decrease the computational effort, thus reducing the carbon footprint of the study, while obtaining great accuracy. Finally, we demonstrate that transfer learning techniques can decrease the computing cost even further, being the carbon footprint of the study just 0.18 g of CO 2 (whereas a single device TCAD study can produce up to 2.6 kg of CO 2 ), while obtaining coefficient of determination values larger than 0.985 when using only a 10% of the input samples.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0288964
DOI:
10.1371/journal.pone.0288964.g001
DOI:
10.1371/journal.pone.0288964.g002
DOI:
10.1371/journal.pone.0288964.g003
DOI:
10.1371/journal.pone.0288964.g004
DOI:
10.1371/journal.pone.0288964.g005
DOI:
10.1371/journal.pone.0288964.g006
DOI:
10.1371/journal.pone.0288964.g007
DOI:
10.1371/journal.pone.0288964.g008
DOI:
10.1371/journal.pone.0288964.g009
DOI:
10.1371/journal.pone.0288964.g010
DOI:
10.1371/journal.pone.0288964.t001
DOI:
10.1371/journal.pone.0288964.t002
DOI:
10.1371/journal.pone.0288964.t003
DOI:
10.1371/journal.pone.0288964.t004
DOI:
10.1371/journal.pone.0288964.t005
DOI:
10.1371/journal.pone.0288964.t006
DOI:
10.1371/journal.pone.0288964.t007
DOI:
10.1371/journal.pone.0288964.s001
DOI:
10.1371/journal.pone.0288964.r001
DOI:
10.1371/journal.pone.0288964.r002
DOI:
10.1371/journal.pone.0288964.r003
DOI:
10.1371/journal.pone.0288964.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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