Numerical Investigation on Microfluidic Electroless Deposition for Uniform Copper Pillar Microbumps Interconnection

Yonglin Zhang, Haibin Chen, H. Fan, Jinglei Yang, Jingshen Wu
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Abstract

The conventional thermo-compression bonding method in either solder-based or solder-less approaches for the 3D chip integration lead to reliability issues including warpage, delamination and die crack due to high temperature and pressure. To eliminate the issues, an approach of microfluidic electroless interconnection featured with low temperature and pressure has been reported. In this work, the multi-physical field model was firstly developed to understand the deposition mechanism of the microfluidic electroless interconnection method based on a simulation framework considering electrochemistry, fluid flow and mass transfer, and experimental validation was conducted. The results of the numerical work manifest good agreement with the experimental data, and the dominant limitation of the technology is insufficient mass transfer in the microchannel introducing deposition thickness non-uniformity reaching 90%. To eliminate the non-uniformity, the effects of flow velocity and reverse flow are investigated demonstrating remarkable enhancement. The theoretical simulation model shows good feasibility and accuracy providing insight and understanding in the process and mechanism of the technology.
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均匀铜柱微凸点互连微流控化学沉积数值研究
传统的基于焊料或无焊料的3D芯片集成热压结合方法会导致可靠性问题,包括翘曲、分层和高温高压导致的模具裂纹。为了消除这些问题,提出了一种低温低压微流控化学互连方法。本文首先在考虑电化学、流体流动和传质的模拟框架下,建立了多物理场模型来理解微流体化学互连方法的沉积机理,并进行了实验验证。数值计算结果与实验数据吻合较好,该技术的主要缺陷是微通道内传质不足,导致沉积厚度不均匀性达到90%。为了消除非均匀性,研究了流速和逆流对非均匀性的影响。理论仿真模型显示了良好的可行性和准确性,为深入了解该技术的过程和机理提供了依据。
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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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