Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Device simulation has been explored and industrialized for over 40 years; however, it still requires huge computational cost. Therefore, it can be further advanced using deep learning (DL) algorithms. We for the first time report an efficient and accurate DL approach with device simulation for gate-...

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Veröffentlicht in:	IEEE access Jg. 9; S. 73467 - 73481
Hauptverfasser:	Akbar, Chandni, Li, Yiming, Sung, Wen-Li
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms artificial neural network Artificial neural networks Computer simulation Computing costs convolutional neural network Deep learning Electric variables Errors Field effect transistors Fluctuations Gallium arsenide gate-all-around Learning theory long short term memory Machine learning Metal oxide semiconductors MOSFET MOSFETs nanosized metal grain nanowire Nanowires Neural networks Predictive models root mean squared error Semiconductor devices Silicon Simulation statistical device simulation Threshold voltage Transistors Work function fluctuation Work functions
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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