Fracture Modeling and Characterization of Underfill/Polymer Interfacial Adhesion in RDL Interposer Package

S. Yeh, P. Lin, C. Hsu, Y. S. Lin, J. H. Wang, P. Lai, C. H. Chen, Y. Lee, M. Yew, S. Cheng, S. Jeng
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引用次数: 2

Abstract

In order to ensure good performance and long-term reliability of fan-out package, the interfacial strength of Underfill (UF) and polymer (PM) lamination plays an important role because of physical strength and electrical requirement. Accordingly, the present study presents a combined experimental and finite element modeling approach for quantitatively determining the interfacial adhesive strength of UF-PM structures. In the proposed approach, four points bending (FPB) testing is used to evaluate the adhesion strength between UF-PM. The test results are used to determine the critical strain energy release rate ($G_{c}$) at the UF-PM interface. The experimental results are then taken as a reference for finite element (FE) simulations. The virtual crack closure technique (VCCT) in FE model is introduced here for risk assessment such as delamination or crack risk at the interface of UF-PM. In general, the results confirm that the proposed predictive modeling approach provides an effective means of evaluating the delamination risk in UF-PM systems. As such, it provides a convenient and cost-effective method for evaluating the new material interface of UF-PM.
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RDL中间层充填体/聚合物界面黏附裂缝建模与表征
为了保证扇形封装的良好性能和长期可靠性,下填料(UF)和聚合物(PM)层合材料的界面强度因其物理强度和电学要求而起着重要的作用。因此,本研究提出了一种结合实验和有限元建模的方法来定量确定UF-PM结构的界面粘接强度。在该方法中,采用四点弯曲(FPB)测试来评估UF-PM之间的粘附强度。试验结果用于确定UF-PM界面的临界应变能释放率($G_{c}$)。实验结果可作为有限元模拟的参考。本文将有限元模型中的虚拟裂纹闭合技术(VCCT)引入到UF-PM界面的分层或裂纹风险评估中。总体而言,结果证实了所提出的预测建模方法为UF-PM系统分层风险评估提供了有效手段。从而为UF-PM的新材料界面评价提供了一种方便、经济的方法。
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Magnetically Actuated Test Method for Interfacial Fracture Reliability Assessment nSiP(System in Package) Platform for various module packaging applications IEEE 71st Electronic Components and Technology Conference [Title page] Evaluation of Low-k Integration Integrity Using Shear Testing on Sub-30 Micron Micro-Cu Pillars CoW Package Solution for Improving Thermal Characteristic of TSV-SiP for AI-Inference
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