Artificial Neural Network based Modelling for Variational Effect on Double Metal Double Gate Negative Capacitance FET
Furkejuvvon:
| Publikašuvnnas: | arXiv.org (Dec 18, 2024), p. n/a |
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| Váldodahkki: | |
| Eará dahkkit: | , , |
| Almmustuhtton: |
Cornell University Library, arXiv.org
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| Fáttát: | |
| Liŋkkat: | Citation/Abstract Full text outside of ProQuest |
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|---|---|---|---|
| 001 | 3147568524 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3147568524 | ||
| 045 | 0 | |b d20241218 | |
| 100 | 1 | |a Pathak, Yash | |
| 245 | 1 | |a Artificial Neural Network based Modelling for Variational Effect on Double Metal Double Gate Negative Capacitance FET | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 18, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a In this work, we have implemented an accurate machine-learning approach for predicting various key analog and RF parameters of Negative Capacitance Field-Effect Transistors (NCFETs). Visual TCAD simulator and the Python high-level language were employed for the entire simulation process. However, the computational cost was found to be excessively high. The machine learning approach represents a novel method for predicting the effects of different sources on NCFETs while also reducing computational costs. The algorithm of an artificial neural network can effectively predict multi-input to single-output relationships and enhance existing techniques. The analog parameters of Double Metal Double Gate Negative Capacitance FETs (D2GNCFETs) are demonstrated across various temperatures (\(T\)), oxide thicknesses (\(T_{ox}\)), substrate thicknesses (\(T_{sub}\)), and ferroelectric thicknesses (\(T_{Fe}\)). Notably, at \(T=300K\), the switching ratio is higher and the leakage current is \(84\) times lower compared to \(T=500K\). Similarly, at ferroelectric thicknesses \(T_{Fe}=4nm\), the switching ratio improves by \(5.4\) times compared to \(T_{Fe}=8nm\). Furthermore, at substrate thicknesses \(T_{sub}=3nm\), switching ratio increases by \(81\%\) from \(T_{sub}=7nm\). For oxide thicknesses at \(T_{ox}=0.8nm\), the ratio increases by \(41\%\) compared to \(T_{ox}=0.4nm\). The analysis reveals that \(T_{Fe}=4nm\), \(T=300K\), \(T_{ox}=0.8nm\), and \(T_{sub}=3nm\) represent the optimal settings for D2GNCFETs, resulting in significantly improved performance. These findings can inform various applications in nanoelectronic devices and integrated circuit (IC) design. | |
| 653 | |a Integrated circuits | ||
| 653 | |a Nanotechnology devices | ||
| 653 | |a Field effect transistors | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Ferroelectric materials | ||
| 653 | |a Neural networks | ||
| 653 | |a Nanoelectronics | ||
| 653 | |a Capacitance | ||
| 653 | |a Visual fields | ||
| 653 | |a Computing costs | ||
| 653 | |a Algorithms | ||
| 653 | |a High level languages | ||
| 653 | |a Python | ||
| 653 | |a Ferroelectricity | ||
| 653 | |a Semiconductor devices | ||
| 653 | |a Machine learning | ||
| 653 | |a Visual effects | ||
| 653 | |a Leakage current | ||
| 653 | |a Parameters | ||
| 653 | |a Thickness | ||
| 700 | 1 | |a Laxman Prasad Goswami | |
| 700 | 1 | |a Bansi Dhar Malhotra | |
| 700 | 1 | |a Chaujar, Rishu | |
| 773 | 0 | |t arXiv.org |g (Dec 18, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3147568524/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.14216 |