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
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Zusammenfassung: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3079981