Chu Xianglong, Wang Shitao, Li Chunlei, Wang Zhizhen, Ma Shenglin, Wu Daowei, Yuan Hai, You Bin
Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361005, China.
31002 PLA Troops, Beijing 100161, China.
Micromachines (Basel). 2025 May 15;16(5):582. doi: 10.3390/mi16050582.
The development of chip manufacturing and advanced packaging technologies has significantly changed redistribution layers (RDLs), leading to shrinking line width/spacing, increasing the number of build-up layers and package size, and introducing organic materials such as polyimide (PI) for dielectrics. The fineness and complexity of structures, combined with the temperature-dependent and viscoelastic properties of organic materials, make it increasingly difficult to predict the thermo-mechanical behavior of wafer-level Cu-PI RDL structures, posing a severe challenge in warpage prediction. This study models and simulates the thermo-mechanical response during the manufacturing process of Cu-PI RDL at the wafer level. A cross-scale wafer-level equivalent model was constructed using a two-level partitioning method, while the PI material properties were extracted via inverse fitting based on thermal warpage measurements. The warpage prediction results were compared against experimental data using the maximum warpage as the indicator to validate the extracted PI properties, yielding errors under less than 10% at typical process temperatures. The contribution of RDL build-up, wafer backgrinding, chemical mechanical polishing (CMP), and through-silicon via (TSV)/through-glass via (TGV) interposers to the warpage was also analyzed through simulation, providing insight for process risk evaluation. Finally, an artificial neural network was developed to correlate the copper ratios of four RDLs with the wafer warpages for a specific process scenario, demonstrating the potential for wafer-level warpage control through copper ratio regulation in RDLs.
芯片制造和先进封装技术的发展显著改变了再分布层(RDL),导致线宽/间距缩小、积层数量和封装尺寸增加,并引入了聚酰亚胺(PI)等有机材料作为电介质。结构的精细度和复杂性,加上有机材料的温度依赖性和粘弹性特性,使得预测晶圆级铜-聚酰亚胺RDL结构的热机械行为变得越来越困难,这在翘曲预测方面构成了严峻挑战。本研究对晶圆级铜-聚酰亚胺RDL制造过程中的热机械响应进行建模和模拟。使用两级划分方法构建了一个跨尺度晶圆级等效模型,同时基于热翘曲测量通过反向拟合提取了PI材料特性。以最大翘曲为指标,将翘曲预测结果与实验数据进行比较,以验证提取的PI特性,在典型工艺温度下产生的误差小于10%。还通过模拟分析了RDL积层、晶圆背面研磨、化学机械抛光(CMP)以及硅通孔(TSV)/玻璃通孔(TGV)中介层对翘曲的贡献,为工艺风险评估提供了见解。最后,开发了一种人工神经网络,将特定工艺场景下四个RDL的铜比例与晶圆翘曲相关联,展示了通过调节RDL中的铜比例来控制晶圆级翘曲的潜力。