采用基于神经网络的集成SBD设备性能预测方法的SiC MOSFET

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Xiping Niu¹, Ling Sang¹, Xiaoling Duan²

¹Beijing Institute of Smart Energy, Beijing 102209, China.

|January 25, 2025

概括

这项研究使用神经网络准确预测碳化物 (SiC) MOSFET与集成的肖特基屏障二极管 (SBD) 的性能. 这种方法加速了具有特定性能目标的功率电子设备的设计.

科学领域:

电力电子电力电子电力电子
半导体设备半导体设备
机器学习机器学习

背景情况:

带有集成Schottky屏障二极管 (SBD) 的碳化物 (SiC) MOSFET提供了卓越的性能.
这些设备的传统TCA模拟复杂且耗时.
神经网络在预测半导体设备特性方面表现有前途.

研究的目的:

应用神经网络机器学习来预测SiC SBD-MOSFET的静态和动态特性.
为SiC SBD-MOSFET性能预测开发一种快速而准确的方法.
将卷积神经网络 (CNN) 与传统机器学习方法的有效性进行比较.

主要方法:

模拟和模拟使用Sentaurus TCAD的SiC SBD-MOSFET设备.
生成了 625 个设备结构和样本数据集.
利用神经网络,特别是CNN,基于TCAD数据进行性能预测.

主要成果:

获得了关键参数的低平均平方误差 (MSE) 值:Vth (0.0051),BV (0.0031),R_on (0.0065) 和E (0.0220).
在预测静态和动态特征方面表现出高准确度.
发现CNN在准确性方面明显优于传统的机器学习方法.

结论:

基于神经网络的预测为SiC SBD-MOSFET性能提供了一个快速而准确的方法.
这种方法可以加速满足特定性能目标的设备的设计过程.
该研究强调了机器学习在推进功率半导体研究方面的潜力.

关键词:

这就是SBD SBD.在SiC MOSFET中使用.神经网络的神经网络的神经网络

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