Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 2328-2340 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Power Electronics |
| Volume | 41 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2026 |
Keywords
- Deep learning (DL)
- Schottky barrier diode (SBD)
- dislocation density
- electro–thermal coupling
- gallium nitride (GaN)
- high-power microwave (HPM)
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