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通过子域采样和Taguchi超参数优化为FCCSP开发基于ANN的曲面预测模型

Hsien-Chie Cheng¹, Chia-Lin Ma¹, Yang-Lun Liu¹

¹Department of Aerospace and Systems Engineering, Feng Chia University, Taichung 407, Taiwan.

|July 29, 2023

概括

本研究介绍了一种先进的人工神经网络 (ANN) 模型,用于预测翻转芯片芯片规模包 (FCCSP) 战页. 新的采样和优化技术提高了电子包装可靠性的预测准确性.

科学领域:

材料科学与工程材料科学与工程
计算力学计算力学计算力学
在工程领域的人工智能.

背景情况:

翻转芯片芯片尺寸包装 (FCCSP) 中的工艺诱导的扭曲是电子包装中关键的可靠性问题.
准确预测曲面对于减轻制造缺陷和确保设备性能至关重要.
现有的预测模型往往缺乏复杂的热力学行为所需的效率和准确性.

研究的目的:

开发一个非常准确和高效的基于人工神经网络 (ANN) 的预测模型,用于FCCSP中的过程诱导的曲面.
通过基于子域的新型采样策略和Taguchi超参数优化来增强ANN模型性能.
通过实验测量验证开发的模型,并将其有效性与现有方法进行比较.

主要方法:

一种过程建模方法,结合了环氧成型化合物的粘弹性行为,其特性通过动态机械测量来确定.
使用热力学分析和动态机械分析评估温度依赖的热力学性能.
在ANN算法中开发和应用一种基于子域的新型采样策略和Taguchi超参数优化.

主要成果:

开发的ANN模型准确预测FCCSP曲面,结果与实验测量得到验证.
参数分析确定了影响曲面行为的关键因素,为模型的构建提供了信息.
拟议的采样和超参数调整方法在与现有模型相比显示出更高的性能.

结论:

该研究成功建立了一个基于ANN的强大的深度学习模型,用于预测FCCSP曲面.
新的采样策略和超参数优化显著提高了预测准确性和效率.
经过验证的模型为优化FCCSP设计和制造流程提供了可靠的工具.

关键词:

人工神经网络的人工神经网络翻转芯片芯片规模的包装超参数优化超参数优化过程建模过程建模采样策略采样策略粘性弹性粘性弹性战争页预测模型的预测模型

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