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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Published in:	IEEE access Vol. 9; pp. 73467 - 73481
Main Authors:	Akbar, Chandni, Li, Yiming, Sung, Wen-Li
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
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Abstract	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-all-around silicon nanowire metal-oxide-semiconductor field-effect transistors (MOSFETs) to predict electrical characteristics of device induced by work function fluctuation. By using three different DL models: artificial neural network (ANN), convolutional neural network (CNN), and long short term memory (LSTM), the variability of threshold voltage, on-current and off-current is predicted with respect to different metal-grain number and location of the low and high values of work function. The comparison is established among the ANN, CNN and the LSTM models and results depict that the CNN model outperforms in terms of the root mean squared error and the percentage error rate. The integration of device simulation with DL models exhibits the characteristic estimation of the explored device efficiently; and, the accurate prediction from the DL models can accelerate the process of device simulation. Notably, the DL approach is able to extract crucial electrical characteristics of a complicated device accurately with 2% error in a cost-effective manner computationally.
AbstractList	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-all-around silicon nanowire metal-oxide-semiconductor field-effect transistors (MOSFETs) to predict electrical characteristics of device induced by work function fluctuation. By using three different DL models: artificial neural network (ANN), convolutional neural network (CNN), and long short term memory (LSTM), the variability of threshold voltage, on-current and off-current is predicted with respect to different metal-grain number and location of the low and high values of work function. The comparison is established among the ANN, CNN and the LSTM models and results depict that the CNN model outperforms in terms of the root mean squared error and the percentage error rate. The integration of device simulation with DL models exhibits the characteristic estimation of the explored device efficiently; and, the accurate prediction from the DL models can accelerate the process of device simulation. Notably, the DL approach is able to extract crucial electrical characteristics of a complicated device accurately with 2% error in a cost-effective manner computationally.
Author	Sung, Wen-Li Akbar, Chandni Li, Yiming
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SubjectTerms	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
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Title	Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs
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