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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Journal of the Electron Devices Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10531738/ |
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| Summary: | 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. |
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| ISSN: | 2168-6734 |