TY - JOUR
T1 - Electro–Thermal Coupling Characteristics of GaN SBDs Under HPM Pulses
T2 - Effect of Dislocation Density and Deep Learning Predictive Model
AU - Liu, Peiran
AU - Liu, Dawei
AU - Liang, Shixiong
AU - Cheng, Yining
AU - Li, Xiao
AU - Chen, Yingwei
AU - Su, Donglin
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2026
Y1 - 2026
N2 - Gallium nitride (GaN) Schottky barrier diodes (SBDs), as essential devices in power-electronic systems, are particularly vulnerable to high-power microwave (HPM) pulses. HPM pulses can induce electro–thermal multiphysics coupling effects in the GaN SBDs, potentially leading to device damage or failure. Dislocation density, as a critical parameter of GaN materials, has a significant effect on the performance of GaN SBDs. In this article, we present an electro–thermal multiphysics coupling analysis of GaN SBDs under HPM pulses, with particular emphasis on the influence of dislocation density on this coupling. Furthermore, by integrating deep learning techniques with technology computer-aided design (TCAD) simulations, we propose a deep multihead attention residual network (DMARN) model that enables the rapid and accurate prediction of maximum electric field strength and temperature within GaN SBDs under varying dislocation density configurations and HPM pulse parameters. Through the validation of ablation and comparative experiments, an additional test set, and hardware experiments, the DMARN model demonstrates outstanding performance and generalization ability.
AB - Gallium nitride (GaN) Schottky barrier diodes (SBDs), as essential devices in power-electronic systems, are particularly vulnerable to high-power microwave (HPM) pulses. HPM pulses can induce electro–thermal multiphysics coupling effects in the GaN SBDs, potentially leading to device damage or failure. Dislocation density, as a critical parameter of GaN materials, has a significant effect on the performance of GaN SBDs. In this article, we present an electro–thermal multiphysics coupling analysis of GaN SBDs under HPM pulses, with particular emphasis on the influence of dislocation density on this coupling. Furthermore, by integrating deep learning techniques with technology computer-aided design (TCAD) simulations, we propose a deep multihead attention residual network (DMARN) model that enables the rapid and accurate prediction of maximum electric field strength and temperature within GaN SBDs under varying dislocation density configurations and HPM pulse parameters. Through the validation of ablation and comparative experiments, an additional test set, and hardware experiments, the DMARN model demonstrates outstanding performance and generalization ability.
KW - Deep learning (DL)
KW - Schottky barrier diode (SBD)
KW - dislocation density
KW - electro–thermal coupling
KW - gallium nitride (GaN)
KW - high-power microwave (HPM)
UR - https://www.scopus.com/pages/publications/105017806888
U2 - 10.1109/TPEL.2025.3614847
DO - 10.1109/TPEL.2025.3614847
M3 - 文章
AN - SCOPUS:105017806888
SN - 0885-8993
VL - 41
SP - 2328
EP - 2340
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
IS - 2
ER -