一种基于数字双胞胎的热压缩粘合电极中三维热疲劳裂纹的新预测方法
Qianyu Ren1, Kai Yang1, Zuoen Deng1
1Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China.
PloS one
|December 16, 2025
概括
本研究引入了一个数字双胞胎 (DT) 框架,结合基于物理的模拟和人工智能,准确预测热压缩粘合 (TCB) 电极的3D裂,提高电子制造的可靠性.
科学领域:
- 材料科学与工程 材料科学与工程
- 人工智能在制造业中的应用
- 计算力学 计算力学 计算力学
背景情况:
- 热压缩粘合 (TCB) 电极在电子制造中至关重要.
- 在TCB电极中的热疲劳裂降低了可靠性和生产速度.
- 准确预测3D电极裂对于缓解策略至关重要.
研究的目的:
- 开发一个数字双胞胎 (DT) 框架,整合基于物理的模拟和人工智能 (AI) 来预测3D电极裂.
- 通过数据增强技术,应对有限的破解数据的挑战.
- 建立一个高精度的解决方案,用于TCB电极裂预测和智能维护.
主要方法:
- 开发了一个数字双胞胎 (DT) 框架,使用扩展有限元方法 (XFEM) 进行高保真电极建模.
- 采用自适应更新来模拟在合的电,热和机械负载下的疲劳行为.
- 使用条件变量自编码器 (CVAE) 与位置注意力 (PA) 机制进行数据增强.
- 开发了PA-RePointNet模型,用于使用增强数据预测3D裂形态.
主要成果:
- 与实验结果相比,CVAE模型实现了0.7%至1.3%的低误差率.
- 与PointNet++和PointCNN相比,PA-RePointNet显示出更高的预测准确性和稳定性.
- 实现了高预测性能,MAE为2.8,RMSE为5.1和R2为0.9378.8的R2.
- 重建和实验3D裂之间的最大相对误差仅为1.87%.
结论:
- 开发的DT框架提供了一个高精度的解决方案,用于预测TCB电极中的3D裂.
- 人工智能的整合,特别是PA-RePointNet模型,有效地解决了数据稀缺问题,并提高了预测准确性.
- 这种方法为微电子制造中的智能维护策略铺平了道路,提高了TCB电极的可靠性.
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