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基于机器学习的2.5D/3D高级包的RDL建模和热力学模拟方法,考虑布局影响,基于机器学习

Xiaodong Wu¹, Zhizhen Wang¹, Shenglin Ma¹

¹Department of Mechanical & Electrical Engineering, Xiamen University, Xiamen 361005, China.

|August 26, 2023

概括

本研究介绍了一种机器学习 (ML) 方法,用于建模和模拟高级包路由 (RDL) 设计. 该方法准确地考虑了布局影响,提高了可靠性并减少了模拟错误.

科学领域:

材料科学材料科学材料科学
电气工程电气工程
计算机科学计算机科学

背景情况:

先进的包装设计面临着可靠性挑战,因为宽度下降,面积比率增加的路由非常 (RDL).
传统的模拟方法很难准确地捕捉复杂的RDL布局对包装可靠性的影响.

研究的目的:

提出和验证基于机器学习 (ML) 的方法用于RDL建模和模拟.
准确预测RDL布局对高级包的可靠性设计 (DFR) 的影响.

主要方法:

将RDL结构数字化为张量器,通过将它们划分为金属百分比的块和像素.
构建和训练基于人工神经网络 (ANN) 的替代模型,以预测等效材料属性.
将块转换为有限元件进行模拟并通过线条曲测试进行验证.

主要成果:

忽视布局影响导致模拟中的关键错误,特别是在稀释基板的情况下.
基于ML的方法实现了2.81%的反应力误差,准确考虑了200×200个元素的布局影响.
热循环测试 (TCT) 模拟显示了RDL的最大应力,RDL和颠都受到布局的严重影响.

结论:

拟议的ML方法精确地考虑了RDL模拟中的布局影响,使用最小的资源.
这种方法提供了一种提高先进包装设计可靠性的有效方法.
高级包装中的最大应力在RDL中比凸起更有可能,受到布局和通道/凸起的影响.

关键词:

具有同等物质性质的材料.布局影响影响的布局.机器学习是机器学习.重新分配层是一个重分配层.热力学模拟的热力学模拟

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