Physics-Based Artificial Neural Network Assisting in Extracting Transient Properties of Extrinsically Triggering Photoconductive Semiconductor Switches
Zhong Zheng, Huiyong Hu, Yutian Wang, Tianlong Zhao, Qian Sun, Hui Guo
TL;DR
This paper introduces a physics-based artificial neural network method to efficiently extract transient properties of photoconductive semiconductor switches.
Contribution
A novel physics-based ANN method is proposed to predict transient properties of PCSSs with high accuracy and reduced computational time.
Findings
The ANN method achieves a mean absolute error of less than 10 A in forecasting transient photocurrent.
The method saves thousands of seconds of CPU runtime compared to traditional simulations.
Bayesian optimization improves the robustness of the Poly-ANN predictor.
Abstract
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…
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Taxonomy
TopicsAdvancements in Semiconductor Devices and Circuit Design · Integrated Circuits and Semiconductor Failure Analysis · Semiconductor Quantum Structures and Devices