Academic Journal
A General Toolkit for Advanced Semiconductor Transistors: From Simulation to Machine Learning
| العنوان: | A General Toolkit for Advanced Semiconductor Transistors: From Simulation to Machine Learning |
|---|---|
| المؤلفون: | Antonio J. Garcia-Loureiro, Natalia Seoane, Julian G. Fernandez, Enrique Comesana |
| المصدر: | IEEE Journal of the Electron Devices Society, Vol 12, Pp 1057-1064 (2024) |
| بيانات النشر: | IEEE, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: | Semiconductor devices, 3D modelling, finite element method, quantum corrections, variability, post-processing tools, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2168-6734 |
| Relation: | https://ieeexplore.ieee.org/document/10531738/; https://doaj.org/toc/2168-6734 |
| DOI: | 10.1109/JEDS.2024.3401852 |
| URL الوصول: | https://doaj.org/article/997e2ed63b834c1ca0ec3684b7555be1 |
| رقم الانضمام: | edsdoj.997e2ed63b834c1ca0ec3684b7555be1 |
| قاعدة البيانات: | Directory of Open Access Journals |
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