基于深度学习的SiC MOSFET设备中单次事件效应的预测
Yixian Guo1, Yingqi Chen1, Xiaozhi Du1,2
1School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China.
Nanotechnology
|November 3, 2025
概括
本研究介绍了一种快速的,基于数据的方法,用于预测碳化 (SiC) MOSFET中的单一事件效应 (SEE). 与传统模拟相比,深度学习模型显著减少了计算时间,提高了可靠性评估.
科学领域:
- * 材料科学与工程 * 材料科学与工程
- * 半导体设备物理学
- *可靠性工程 *可靠性工程
背景情况:
- *单一事件效应 (SEE) 对碳化物 (SiC) 金属氧化物半导体场效应晶体管 (MOSFET) 构成了关键的可靠性挑战,特别是在苛刻的航空航天环境中.
- *传统技术计算机辅助设计 (TCAD) 模拟虽然准确,但在计算上是不可避免的,并且需要专门的专业知识来预测SEE.
- *对于推进SiC MOSFET在高可靠性应用中的使用,需要高效且易于使用的SEE预测方法至关重要.
研究的目的:
- * 开发一种新的数据驱动方法,用于预测SiC MOSFET中的SEE.
- * 显著降低与SEE分析相关的计算成本.
- *为可靠性评估提供高精度和更快的替代传统TCA模拟.
主要方法:
- * 构建一个全面的数据集 (52,920 SEE事件),包括不同的环境温度,离子能量,偏向电压和发生参数.
- * 实施两种深度学习模型:剩余深度神经网络 (RDNN) 用于峰值电流 (I0) 和收集电荷 (Q0) 的预测,以及卷积神经网络门循环单元 (CNN-GRU) 用于短暂电流脉冲的预测.
- * 应用对称日志互换数据缩放技术,以优化模型预处理.
主要成果:
- * RDNN模型实现了高预测准确度,I0和Q0.0的R2为0.99864
- *CNN-GRU模型在预测排水短暂电流脉冲方面表现出色,达到0.99783.3的R2.
- * 拟议的数据驱动模型与传统的TCAD模拟相比,提供了五到六个数量级的加速.
结论:
- *开发的数据驱动方法为SiC MOSFET中的SEE预测提供了高度准确和计算效率高的解决方案.
- *这种方法大大减少了可靠性分析所需的时间和资源,使其更容易获得.
- *这些发现表明,在SiC MOSFET和潜在的其他半导体设备中,SEE预测是一个有希望的替代方案,加速了关键应用中的开发和部署.
相关概念视频
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