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使用机器学习和有限元分析,在绝缘门双极晶体管模块的结合线接口上提取引-分离关系
Shengjun Zhao1,2, Tong An1,2, Qi Wang1,2
1Institute of Electronics Packaging Technology and Reliability, School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing 100124, China.
Materials (Basel, Switzerland)
|March 13, 2024
概括
本研究引入了一种结合CNN和LSTM的新型机器学习模型,以准确预测绝缘门双极晶体管 (IGBT) 模块中粘接接口的机械性质. 这种方法通过改善关键故障机制的分析来提高IGBT的可靠性.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 绝缘门双极晶体管 (IGBT) 模块是关键功率电子设备.
- 电线结合是IGBT模块中常见的互连方法.
- 粘接接口的破裂是影响IGBT模块可靠性的主要故障机制.
研究的目的:
- 研究IGBT模块中粘接接口的机械性能.
- 开发一种准确和有效的方法,用于确定有限元素-凝聚性区域模型 (FE-CZM) 中的引分离定律 (TSL) 的参数.
- 提出一种新的机器学习架构来分析绑定接口行为.
主要方法:
- 进行了切削测试,以确定粘合接口的强度.
- 建立了有限元素-凝聚区建模 (FE-CZM) 来模拟机械行为.
- 开发了一个混合卷积神经网络 (CNN) 和长短期记忆 (LSTM) 机器学习架构.
- 该CNN-LSTM模型使用负载位移数据进行训练,以预测TSL形状和参数.
主要成果:
- 在CNN-LSTM模型中,TSL形状预测错误率仅为0.186%.
- 对于TSL参数预测的平均绝对百分比误差 (MAPE) 低于3.5044%.
- 拟议的CNN-LSTM架构与独立的CNN或LSTM模型相比显示出更高的性能.
结论:
- 集成FE-CZM和新的CNN-LSTM机器学习方法为IGBT模块中粘接接口的表征提供了有效的解决方案.
- 这种方法提高了机械性能的评估,有助于提高IGBT模块的可靠性.
- 该研究强调了先进的ML技术在材料科学和机械工程应用中的潜力.
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