MLFoMpy: A post-processing tool for semiconductor TCAD data with machine-learning capabilities

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dc.contributor.affiliationUniversidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Electrónica e Computación
dc.contributor.authorComesaña Figueroa, Enrique
dc.contributor.authorGarcía Fernández, Julián
dc.contributor.authorSeoane Iglesias, Natalia
dc.contributor.authorGarcía Loureiro, Antonio Jesús
dc.date.accessioned2026-01-22T07:42:10Z
dc.date.available2026-01-22T07:42:10Z
dc.date.issued2025-05
dc.description.abstractWe present MLFoMpy, a Python package for post-processing data from semiconductor device simulations. The software automatically extracts key figures of merit from current–voltage curves of field effect transistor and calculates statistical analyses for these curves. MLFoMpy also includes machine learning tools to predict figures of merit and current–voltage curves for devices with intrinsic variability. Additionally, it offers data visualization tools to plot current–voltage curves and statistical graphs.
dc.description.peerreviewedSI
dc.description.sponsorshipThe work presented in this paper is funded by the Xunta de Galicia and FEDER funds (ED431F 2020/008, ED431C 2022/16) and by the Agencia Estatal de Investigación (PID2022-141623NB-I00, PID2022-142709OB-C21/PID2022-142709OA-C22).
dc.identifier.citationSoftwareX Volume 30, May 2025, 102166
dc.identifier.doi10.1016/j.softx.2025.102166
dc.identifier.issn2352-7110
dc.identifier.urihttps://hdl.handle.net/10347/45320
dc.issue.number102166
dc.journal.titleSoftwareX
dc.language.isoeng
dc.publisherElsevier
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-141623NB-I00/ES/COMPUTACION DE ALTAS PRESTACIONES, HETEROGENEA Y EN LA NUBE PARA APLICACIONES DE ALTA DEMANDA
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-142709OB-C21/ES/REMOVIRT H3D: RECONSTRUCCION Y MODELADO VIRTUAL HEPATICO 3D
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-142709OA-C22/ES/RECONSTRUCCION Y MODELADO VIRTUAL HEPATICO 3D
dc.relation.publisherversionhttps://doi.org/10.1016/j.softx.2025.102166
dc.rights© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC license. Attribution-NonCommercial 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectSemiconductor device modeling
dc.subjectNanoelectronics
dc.subjectData processing
dc.subjectMachine learning
dc.subjectPython
dc.subjectPytorch
dc.subject.classification330714 Dispositivos semiconductores
dc.titleMLFoMpy: A post-processing tool for semiconductor TCAD data with machine-learning capabilities
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number30
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery3a7c31d3-5d61-4414-a6ae-b129a353f543

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