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Deep-learning Model for Buildup of Ionization Defects in Bipolar Junction Transistors

Li, Lei ; Chen, Xiao-Chi ; Yang, Gui-Xia

IEEE transactions on nuclear science, 2023-09, Vol.70 (9), p.1-1 [Periódico revisado por pares]

New York: IEEE

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Título:
Deep-learning Model for Buildup of Ionization Defects in Bipolar Junction Transistors
Autor: Li, Lei ; Chen, Xiao-Chi ; Yang, Gui-Xia
Assuntos: Artificial Neural Network ; Artificial neural networks ; Bipolar Junction Transistors ; Bipolar transistors ; Data models ; Deep learning ; Defects ; Dosage ; Dose rate effects ; Hydrogen ; Integrated circuit modeling ; Interface states ; Ionization ; Ionization Defects ; Junction transistors ; Machine Learning ; Neural networks ; Neurons ; Numerical models ; Semiconductor devices ; Transistors
É parte de: IEEE transactions on nuclear science, 2023-09, Vol.70 (9), p.1-1
Descrição: The ionization defects in bipolar junction transistors (BJTs) are sensitive to ionization dose rate and molecular hydrogen in oxides. The bimolecular reaction model, a physical model with self-coincidence forms, is established in earlier works to describe formations of ionization defects in BJTs. This work proposes a deep-learning model with artificial neural network (ANN) framework to calculate the densities of ionization defects. The ANN model can be trained using data prepared by the physical model in conjunction with numerical calculations. Case-studies with a dataset of ~6.0×10 5 points suggest an optimized ANN model which is composed of four hidden layers and sixteen ELU-activated (Exponential Linear Unit) neurons per hidden layer. This model provides excellent fits to the data from physical model for the dose-, dose-rate- and hydrogen-dependence of ionization defects in gate-controlled p-n-p BJTs. Our encouraging results show that the deep-learning modeling methodology may find its effective application in dynamics of ionization defects.
Editor: New York: IEEE
Idioma: Inglês

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Deep-learning Model for Buildup of Ionization Defects in Bipolar Junction Transistors

Li, Lei ; Chen, Xiao-Chi ; Yang, Gui-Xia

New York: IEEE

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