A General Toolkit for Advanced Semiconductor Transistors: From Simulation to Machine Learning
This work presents an overview of a set of in-house-built software tools intended for state-of-the-art semiconductor device modelling, ranging from numerical simulators to post-processing tools and prediction codes based on statistics and machine learning techniques. First, VENDES is a 3D finite-ele...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of the Electron Devices Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10531738/ |
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| author | Antonio J. Garcia-Loureiro Natalia Seoane Julian G. Fernandez Enrique Comesana |
| author_facet | Antonio J. Garcia-Loureiro Natalia Seoane Julian G. Fernandez Enrique Comesana |
| author_sort | Antonio J. Garcia-Loureiro |
| collection | DOAJ |
| description | This work presents an overview of a set of in-house-built software tools intended for state-of-the-art semiconductor device modelling, ranging from numerical simulators to post-processing tools and prediction codes based on statistics and machine learning techniques. First, VENDES is a 3D finite-element based quantum-corrected semi-classical/classical toolbox able to characterise the performance, scalability, and variability of transistors. MLFoMPy is a Python-based tool that post-processes IV characteristics, extracting the most relevant figures of merit and preparing the data for subsequent statistical or machine learning studies. FSM is a variability prediction tool that also pinpoints the most sensitive regions of a device to a specific source of fluctuation. Finally, we also describe machine learning-based prediction tools that were used to obtain full IV curves and specific figures of merit of devices suffering the influence of several sources of variability. |
| format | Article |
| id | doaj-art-997e2ed63b834c1ca0ec3684b7555be1 |
| institution | Kabale University |
| issn | 2168-6734 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of the Electron Devices Society |
| spelling | doaj-art-997e2ed63b834c1ca0ec3684b7555be12025-01-01T00:00:49ZengIEEEIEEE Journal of the Electron Devices Society2168-67342024-01-01121057106410.1109/JEDS.2024.340185210531738A General Toolkit for Advanced Semiconductor Transistors: From Simulation to Machine LearningAntonio J. Garcia-Loureiro0https://orcid.org/0000-0003-0574-1513Natalia Seoane1https://orcid.org/0000-0003-0973-461XJulian G. Fernandez2https://orcid.org/0000-0002-1347-4577Enrique Comesana3https://orcid.org/0000-0001-8422-5394Centro Singular de Investigación en Tecnoloxías Intelixentes, Universidade de Santiago de Compostela, Santiago de Compostela, SpainCentro Singular de Investigación en Tecnoloxías Intelixentes, Universidade de Santiago de Compostela, Santiago de Compostela, SpainCentro Singular de Investigación en Tecnoloxías Intelixentes, Universidade de Santiago de Compostela, Santiago de Compostela, SpainEscola Politécnica Superior de Enxeñaría, Universidade de Santiago de Compostela, Santiago de Compostela, SpainThis work presents an overview of a set of in-house-built software tools intended for state-of-the-art semiconductor device modelling, ranging from numerical simulators to post-processing tools and prediction codes based on statistics and machine learning techniques. First, VENDES is a 3D finite-element based quantum-corrected semi-classical/classical toolbox able to characterise the performance, scalability, and variability of transistors. MLFoMPy is a Python-based tool that post-processes IV characteristics, extracting the most relevant figures of merit and preparing the data for subsequent statistical or machine learning studies. FSM is a variability prediction tool that also pinpoints the most sensitive regions of a device to a specific source of fluctuation. Finally, we also describe machine learning-based prediction tools that were used to obtain full IV curves and specific figures of merit of devices suffering the influence of several sources of variability.https://ieeexplore.ieee.org/document/10531738/Semiconductor devices3D modellingfinite element methodquantum correctionsvariabilitypost-processing tools |
| spellingShingle | Antonio J. Garcia-Loureiro Natalia Seoane Julian G. Fernandez Enrique Comesana A General Toolkit for Advanced Semiconductor Transistors: From Simulation to Machine Learning IEEE Journal of the Electron Devices Society Semiconductor devices 3D modelling finite element method quantum corrections variability post-processing tools |
| title | A General Toolkit for Advanced Semiconductor Transistors: From Simulation to Machine Learning |
| title_full | A General Toolkit for Advanced Semiconductor Transistors: From Simulation to Machine Learning |
| title_fullStr | A General Toolkit for Advanced Semiconductor Transistors: From Simulation to Machine Learning |
| title_full_unstemmed | A General Toolkit for Advanced Semiconductor Transistors: From Simulation to Machine Learning |
| title_short | A General Toolkit for Advanced Semiconductor Transistors: From Simulation to Machine Learning |
| title_sort | general toolkit for advanced semiconductor transistors from simulation to machine learning |
| topic | Semiconductor devices 3D modelling finite element method quantum corrections variability post-processing tools |
| url | https://ieeexplore.ieee.org/document/10531738/ |
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