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解释基于人工神经网络的4个H-SiC mosfets的建模,使用可解释的AI

Yu-Sheng Hsiao¹, Pei-Jie Chang², Bang-Ren Chen³

¹Institute of Pioneer Semiconductor Innovation, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.

Scientific reports

|January 16, 2026

概括

本研究介绍了一个可解释的AI框架,用于建模4H-碳酸金属氧化物半导体场效应晶体管 (SiC MOSFETs). 它准确地预测设备性能和解释设计影响,增强功率电子开发.

科学领域:

材料科学材料科学材料科学
电气工程电气工程
计算机科学计算机科学

背景情况:

像4H-SiC MOSFET这样的宽带间隙 (WBG) 半导体对于先进的功率电子非常重要,提供高效率和热稳定性.
这些设备的传统技术计算机辅助设计 (TCAD) 模拟是计算密集的,缺乏可扩展性.
工艺变化对WBG半导体的电性能产生重大影响.

研究的目的:

开发一种新的,可解释的机器学习框架,用于准确和可解释的4H-SiC MOSFET设备建模.
为了解决传统TCA模拟的计算限制和可扩展性问题.
为了解和优化半导体设备设计提供透明,数据驱动的方法.

主要方法:

人工神经网络 (ANN) 与可解释的人工智能 (XAI) 技术的整合,特别是夏普利添加式扩展 (SHAP).
在广泛的TCAD生成数据集上训练ANN,这些数据集涵盖了各种结构和兴奋剂参数.
使用SHAP来量化个别设计参数对设备电气特性的影响.

主要成果:

拟议的模型实现了比0.99高的皮尔森相关系数,用于预测现状电流.
SHAP分析表明了物理上一致的关系,例如排水流和氧化物厚度/通道长度之间的逆相关性.
该框架成功地为SiC MOSFETs的设计性能相关性提供了可解释的见解.

结论:

这项研究建立了一个透明和数据驱动的框架,用于使用可解释的AI来理解和优化SiC MOSFET.
该方法为设备建模提供了一个可扩展和准确的替代传统TCAD.
该方法可适应其他半导体技术,在设备建模中需要高精度和可解释性.

关键词:

设备设计设备设计可解释的人工智能机器学习是机器学习.在SiC mosfets中使用.

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