基于神经网络的GaN HEMT切换电流模型的研究
Xiang Wang1, Zhihui Zhao2, Huikai Chen2
1School of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Micromachines
|August 28, 2025
概括
结合卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 的新型混合深度学习模型准确预测了GaN HEMT切换电流,克服了传统模型的局限性.
科学领域:
- 电气工程
- 材料科学
- 人工智能
背景情况:
- 化高电子移动性晶体管 (GaN HEMT) 装置显示复杂的非线性切换动态.
- 传统的物理模型难以准确地捕捉这些复杂的多物理合特性.
- 现有的模型在预测切换当前行为方面存在局限性.
研究的目的:
- 开发一个更准确,更有效的模型来预测GaN HEMT切换电流.
- 解决传统物理模型在捕获复杂设备动态方面的局限性.
- 利用深度学习来增强半导体设备的行为模型.
主要方法:
- 采用混合深度学习架构,集成1D卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 层.
- 1D-CNN层使用滑动卷积内核从时间序列数据中提取本地短暂特征.
- 双层LSTM网络通过关门机制和状态转移捕获短暂的电流特征.
主要成果:
- 与传统模型相比,拟议的CNN-LSTM模型显著降低了根平均平方误差 (RMSE) 和平均绝对误差 (MAE).
- 该模型在室内和高温下表现出色,系数接近1.
- 混合架构实现了高精度,同时保持了短的运行时间和低的计算资源消耗.
结论:
- 混合CNN-LSTM模型为准确预测GaN HEMT切换电流提供了卓越的解决方案.
- 这种深度学习方法克服了传统建模技术的精度和复杂性的局限性.
- 该模型的效率和性能使其适用于实际的工程应用.
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