Physics-Based Artificial Neural Network Assisting in Extracting Transient Properties of Extrinsically Triggering Photoconductive Semiconductor Switches

In this paper, a physics-based ANN assisting method for extracting transient properties of extrinsically triggering photoconductive semiconductor switches (ET-PCSSs) is proposed. It exploits the nonlinear mapping of ANN between transient current (input) and doping concentration (output). According t...

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Bibliographic Details
Published in:Micromachines (Basel) 2024-08, Vol.15 (8), p.1003
Main Authors: Zheng, Zhong, Hu, Huiyong, Wang, Yutian, Zhao, Tianlong, Sun, Qian, Guo, Hui
Format: Article
Language:English
Subjects:
Artificial neural networks
CAD
Computer aided design
Electric fields
Electrodes
extrinsically triggering photoconductive semiconductor switch (ET-PCSS)
Laws, regulations and rules
Mixed mode simulation
Neural networks
Nitrogen
Photoelectric effect
Photoelectric emission
Photoelectricity
Physics
physics-based neural network (NN)
Polynomials
regression fitting
Simulation
Spectrum analysis
Switches
Transient current
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Summary:In this paper, a physics-based ANN assisting method for extracting transient properties of extrinsically triggering photoconductive semiconductor switches (ET-PCSSs) is proposed. It exploits the nonlinear mapping of ANN between transient current (input) and doping concentration (output). According to the basic laws of photoelectric device operating, two types of ANN models are constructed by gaussian and polynomial fitting. The mean absolute error (MAE) of forecasting transient photocurrent can be less than 10 A under low triggering optical powers, which verifies the feasibility of ANN assisting TCAD applied to PCSSs. The results are comparable to computation by Mixed-Mode simulation, yet even thousands of seconds of CPU runtime cost are saved in every period. To improve the robustness of the Poly-ANN predictor, Bayesian optimization (BO) is implemented for minimizing the curl deviation of photocurrent-time curves.
ISSN:2072-666X
2072-666X
DOI:10.3390/mi15081003