Machine Learning-Based Modeling of Hot Carrier Injection in 40 nm CMOS Transistors
This paper presents a machine-learning-based approach for the degradation modeling of hot carrier injection in metal-oxide-semiconductor field-effect transistors (MOSFETs). Stress measurement data have been employed at various stress conditions of both n- and p-MOSFETs with different channel geometr...
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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/10477498/ |
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| author | Xhesila Xhafa Ali Dogus Gungordu Mustafa Berke Yelten |
| author_facet | Xhesila Xhafa Ali Dogus Gungordu Mustafa Berke Yelten |
| author_sort | Xhesila Xhafa |
| collection | DOAJ |
| description | This paper presents a machine-learning-based approach for the degradation modeling of hot carrier injection in metal-oxide-semiconductor field-effect transistors (MOSFETs). Stress measurement data have been employed at various stress conditions of both n- and p-MOSFETs with different channel geometries. Gaussian process regression algorithm is preferred to model the post-stress characteristics of the drain-source current, the threshold voltage, and the drain-source conductance. The model outcomes have been compared with the actual measurements, and the accuracy of the generated models has been demonstrated across the test data by providing the appropriate statistics metrics. Finally, case studies of degradation estimation have been considered involving the usage of machine-learning-based models on transistors with different channel geometries or subjected to distinct stress conditions. The outcomes of this analysis reveal that the established models yield high accuracy in such contexts. |
| format | Article |
| id | doaj-art-42728040a3d64e968f403d6a587a2a49 |
| 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-42728040a3d64e968f403d6a587a2a492025-01-28T00:00:24ZengIEEEIEEE Journal of the Electron Devices Society2168-67342024-01-011228128810.1109/JEDS.2024.338057210477498Machine Learning-Based Modeling of Hot Carrier Injection in 40 nm CMOS TransistorsXhesila Xhafa0https://orcid.org/0000-0001-8951-7580Ali Dogus Gungordu1https://orcid.org/0000-0002-6921-8142Mustafa Berke Yelten2https://orcid.org/0000-0001-7482-0536LIRMM, CNRS, Montpellier, FranceDepartment of Electronics and Communications Engineering, Istanbul Technical University, Istanbul, TurkeyDepartment of Electronics and Communications Engineering, Istanbul Technical University, Istanbul, TurkeyThis paper presents a machine-learning-based approach for the degradation modeling of hot carrier injection in metal-oxide-semiconductor field-effect transistors (MOSFETs). Stress measurement data have been employed at various stress conditions of both n- and p-MOSFETs with different channel geometries. Gaussian process regression algorithm is preferred to model the post-stress characteristics of the drain-source current, the threshold voltage, and the drain-source conductance. The model outcomes have been compared with the actual measurements, and the accuracy of the generated models has been demonstrated across the test data by providing the appropriate statistics metrics. Finally, case studies of degradation estimation have been considered involving the usage of machine-learning-based models on transistors with different channel geometries or subjected to distinct stress conditions. The outcomes of this analysis reveal that the established models yield high accuracy in such contexts.https://ieeexplore.ieee.org/document/10477498/Reliabilityhot carrier injectionHCImachine learningMLGaussian process regressions |
| spellingShingle | Xhesila Xhafa Ali Dogus Gungordu Mustafa Berke Yelten Machine Learning-Based Modeling of Hot Carrier Injection in 40 nm CMOS Transistors IEEE Journal of the Electron Devices Society Reliability hot carrier injection HCI machine learning ML Gaussian process regressions |
| title | Machine Learning-Based Modeling of Hot Carrier Injection in 40 nm CMOS Transistors |
| title_full | Machine Learning-Based Modeling of Hot Carrier Injection in 40 nm CMOS Transistors |
| title_fullStr | Machine Learning-Based Modeling of Hot Carrier Injection in 40 nm CMOS Transistors |
| title_full_unstemmed | Machine Learning-Based Modeling of Hot Carrier Injection in 40 nm CMOS Transistors |
| title_short | Machine Learning-Based Modeling of Hot Carrier Injection in 40 nm CMOS Transistors |
| title_sort | machine learning based modeling of hot carrier injection in 40 nm cmos transistors |
| topic | Reliability hot carrier injection HCI machine learning ML Gaussian process regressions |
| url | https://ieeexplore.ieee.org/document/10477498/ |
| work_keys_str_mv | AT xhesilaxhafa machinelearningbasedmodelingofhotcarrierinjectionin40nmcmostransistors AT alidogusgungordu machinelearningbasedmodelingofhotcarrierinjectionin40nmcmostransistors AT mustafaberkeyelten machinelearningbasedmodelingofhotcarrierinjectionin40nmcmostransistors |