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基于机器学习的SIPOS调制漂移区域U-MOSFET的优点模型

Zhen Cao^1,2, Qi Sun^1,2, Chuanfeng Ma^1,2

¹School of Artificial Intelligence, Xidian University, Xi'an 710126, China.

|March 28, 2024

概括

本研究介绍了一种机器学习模型,用于模块化漂移区域的超连接 (SJ) U-MOSFET. 该模型优化了故障电压和ON电阻之间的权衡,超过了极限.

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
材料科学材料科学材料科学

背景情况:

超连接 (SJ) U-MOSFET对于高压应用至关重要.
优化故障电压 (BV) 和特定ON电阻 (R_on) 之间的权衡是一个关键挑战.
现有的模型可能无法完全捕捉模块化漂移区域中的复杂相互作用.

研究的目的:

为SJ U-MOSFETs与调制漂移区域开发基于机器学习的优点图 (FOM) 模型.
为了优化故障电压 (BV) 和特定ON电阻 (R_on) 的权衡.
在设备性能方面超越SJ极限.

主要方法:

使用半绝缘多晶 (SIPOS) 支柱来调节SJ漂移区域.
开发一个分析模型,考虑电场调制,电荷合和载体积累.
采用高斯过程回归用于FOM预测和超参数优化.
与 TCAD 模拟结果对模型进行验证.

主要成果:

一个强大的机器学习模型用于预测SSJ-UMOSFET的FOM (BV^2/R_on).
确定漂移区域,氧化物厚度和电场调制的最佳结构参数.
展示了一种方法来实现BV-R_on的权衡,超过SJ极限.
使用 TCAD 模拟进行模型的全面验证.

结论:

拟议的机器学习模型准确地预测了模块化漂移区域的SJ U-MOSFET的性能.
该方法允许设计具有优越BV-R_on特性的设备.
这项工作有助于推进高性能功率半导体设备.

关键词:

的极限是的极限.在UMOSFET中使用UMOSFET.机器学习是机器学习.超级连接的超级连接

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