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98 / 2023-09-19 17:06:28
The output characteristic model of SiC DSRD is established by BP neural network
pulsed power device, DSRD, neural network, efficiency
Special Sessions > SS06 - Artificial Intelligence for Semiconductor Modelling and Applications
Final Paper
Ruiheng Tian / Xidian University
Xiao-Yan Tang / Xidian University
Jingkai Guo / Xidian University
Lejia Sun / Xidian University
ZHANG Yuming / Xidian University
Abstract—Drift step recovery diode (DSRD) is a kind of highly nonlinear pulse power devices that exhibits complex inner physics. Traditional modeling approaches often fail to provide accurate and efficient models due to the complexity of its internal mechanisms. In this report, a neural network is employed to establish the relationship between the output characteristics and its trigger condition. By introducing the feature points on the output curve, a prediction model for the output features is established. This approach significantly improves modeling efficiency in engineering applications, circumventing issues such as time-consuming and computationally intensive processes encountered in traditional modeling. The neural model is been established which keeps the average error below 5%.

 
Important Date

  • Conference Date

    Nov 02

    2023

    to

    Nov 04

    2023

  • Dec 15 2023

    Draft paper submission deadline

  • Dec 20 2023

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
Xidian University
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