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多算法集成学习框架,用于预测晶圆级包装的接联接可靠性

Qinghua Su¹, Kuo-Ning Chiang¹

¹Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu City 300044, Taiwan.

Materials (Basel, Switzerland)

|September 13, 2025

概括

本研究使用机器学习 (ML) 与有限元分析 (FEA) 预测包装可靠性. 集体学习框架可以提高有限数据的准确性,降低用于高效设计的计算成本.

科学领域:

材料科学材料科学材料科学
计算机科学计算机科学
机械工程机械工程

背景情况:

传统的设计对实验方法来确定包装的可靠性是昂贵和耗时的.
机器学习 (ML) 提供了有效预测的潜力,但在有限的训练数据和高计算成本的情况下面临着挑战.
经过验证的有限元分析 (FEA) 可以生成模拟数据,但优化使用减少数据集的ML训练至关重要.

研究的目的:

通过使用验证的FEA和ML开发一个有效的包装可靠性的预测方法.
为了应对培训数据集大小减少的挑战,并保持机器学习模型的预测准确性.
提出一个集体学习框架,以进行可靠和准确的包装可靠性预测,使用晶圆级包装 (WLP) 作为案例研究.

主要方法:

利用验证的有限元分析 (FEA) 来生成包装可靠性的模拟数据.
应用机器学习 (ML) 技术,根据FEA生成的数据来预测包装可靠性.
开发和实施集体学习框架,集成多个ML算法,以提高预测稳定性.

主要成果:

整体学习框架证明了改善了包装可靠性预测的概括能力.
即使使用受限制的训练数据,也可以实现准确的预测,克服了小数据ML的局限性.
拟议的方法通过利用各种ML算法的互补优势,有效地提高了预测稳定性.

结论:

集体学习为预测包装可靠性提供了强大的解决方案,使用有限的数据来预测包装可靠性.
这种方法通过减少对昂贵的实验方法的依赖,提高了设计效率.
该框架为包装可靠性评估提供了一种计算效率高,准确的方法.

关键词:

晶圆级包装 (WLP) 是一种包装方式.组合学习组合学习有限元素分析 (FEA)机器学习是机器学习.

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