• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

用于预测晶圆级铜-聚酰亚胺再分布层(Cu-PI RDLs)制造过程中热机械行为的建模与仿真

Modeling and Simulation for Predicting Thermo-Mechanical Behavior of Wafer-Level Cu-PI RDLs During Manufacturing.

作者信息

Chu Xianglong, Wang Shitao, Li Chunlei, Wang Zhizhen, Ma Shenglin, Wu Daowei, Yuan Hai, You Bin

机构信息

Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361005, China.

31002 PLA Troops, Beijing 100161, China.

出版信息

Micromachines (Basel). 2025 May 15;16(5):582. doi: 10.3390/mi16050582.

DOI:10.3390/mi16050582
PMID:40428708
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12114151/
Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/777eb1391e9a/micromachines-16-00582-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/4a1e437b5157/micromachines-16-00582-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/98a46f0cbf39/micromachines-16-00582-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/44656f2d59ce/micromachines-16-00582-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/6c7e64e57c38/micromachines-16-00582-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/217bfd578f70/micromachines-16-00582-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/925ca8070e84/micromachines-16-00582-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/729d5c02c7b5/micromachines-16-00582-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/34810688482d/micromachines-16-00582-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/8fa8cb8d421b/micromachines-16-00582-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/fd8b8c93ac17/micromachines-16-00582-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/fb958f4a7b95/micromachines-16-00582-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/706ef9b40496/micromachines-16-00582-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/0e7e413781a1/micromachines-16-00582-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/910762f72e1a/micromachines-16-00582-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/a0b9596e2517/micromachines-16-00582-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/d242de54fc44/micromachines-16-00582-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/967f7c411315/micromachines-16-00582-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/75f64287ba92/micromachines-16-00582-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/04027b8cc1dc/micromachines-16-00582-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/777eb1391e9a/micromachines-16-00582-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/4a1e437b5157/micromachines-16-00582-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/98a46f0cbf39/micromachines-16-00582-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/44656f2d59ce/micromachines-16-00582-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/6c7e64e57c38/micromachines-16-00582-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/217bfd578f70/micromachines-16-00582-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/925ca8070e84/micromachines-16-00582-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/729d5c02c7b5/micromachines-16-00582-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/34810688482d/micromachines-16-00582-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/8fa8cb8d421b/micromachines-16-00582-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/fd8b8c93ac17/micromachines-16-00582-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/fb958f4a7b95/micromachines-16-00582-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/706ef9b40496/micromachines-16-00582-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/0e7e413781a1/micromachines-16-00582-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/910762f72e1a/micromachines-16-00582-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/a0b9596e2517/micromachines-16-00582-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/d242de54fc44/micromachines-16-00582-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/967f7c411315/micromachines-16-00582-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/75f64287ba92/micromachines-16-00582-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/04027b8cc1dc/micromachines-16-00582-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/64ed/12114151/777eb1391e9a/micromachines-16-00582-g020.jpg

相似文献

1
Modeling and Simulation for Predicting Thermo-Mechanical Behavior of Wafer-Level Cu-PI RDLs During Manufacturing.用于预测晶圆级铜-聚酰亚胺再分布层(Cu-PI RDLs)制造过程中热机械行为的建模与仿真
Micromachines (Basel). 2025 May 15;16(5):582. doi: 10.3390/mi16050582.
2
Investigation of Warpage for Multi-Die Fan-Out Wafer-Level Packaging Process.多芯片扇出型晶圆级封装工艺的翘曲研究
Materials (Basel). 2022 Feb 23;15(5):1683. doi: 10.3390/ma15051683.
3
Warpage Characteristics and Process Development of Through Silicon Via-Less Interconnection Technology.无硅通孔互连技术的翘曲特性与工艺开发
J Nanosci Nanotechnol. 2018 Aug 1;18(8):5558-5565. doi: 10.1166/jnn.2018.15444.
4
Warpage Simulation During Fan-Out Wafer-Level Packaging Process with Uncertainty of Material Properties.基于材料属性不确定性的扇出型晶圆级封装过程中的翘曲模拟
J Nanosci Nanotechnol. 2021 May 1;21(5):2987-2991. doi: 10.1166/jnn.2021.19136.
5
Exploring the Influence of Material Properties of Epoxy Molding Compound on Wafer Warpage in Fan-Out Wafer-Level Packaging.探索扇出型晶圆级封装中环氧模塑料材料特性对晶圆翘曲的影响。
Materials (Basel). 2023 Apr 30;16(9):3482. doi: 10.3390/ma16093482.
6
An Experimental and Numerical Study on Glass Frit Wafer-to-Wafer Bonding.玻璃料晶圆对晶圆键合的实验与数值研究
Micromachines (Basel). 2023 Jan 8;14(1):165. doi: 10.3390/mi14010165.
7
Multi-Step Mechanical and Thermal Homogenization for the Warpage Estimation of Silicon Wafers.用于硅片翘曲估计的多步机械和热均质化
Micromachines (Basel). 2024 Mar 18;15(3):408. doi: 10.3390/mi15030408.
8
An RDL Modeling and Thermo-Mechanical Simulation Method of 2.5D/3D Advanced Package Considering the Layout Impact Based on Machine Learning.一种基于机器学习考虑布局影响的2.5D/3D先进封装的RDL建模与热机械仿真方法
Micromachines (Basel). 2023 Jul 30;14(8):1531. doi: 10.3390/mi14081531.
9
Reliability Simulation Analysis of TSV Structure in Silicon Interposer under Temperature Cycling.硅中介层中TSV结构在温度循环下的可靠性模拟分析
Micromachines (Basel). 2024 Jul 30;15(8):986. doi: 10.3390/mi15080986.
10
Theoretical and Experimental Investigation of Warpage Evolution of Flip Chip Package on Packaging during Fabrication.倒装芯片封装在制造过程中封装翘曲演变的理论与实验研究。
Materials (Basel). 2021 Aug 25;14(17):4816. doi: 10.3390/ma14174816.

本文引用的文献

1
Omnidirectionally Stretchable Spin-Valve Sensor Array with Stable Giant Magnetoresistance Performance.具有稳定巨磁阻性能的全向可拉伸自旋阀传感器阵列
ACS Nano. 2025 Feb 11;19(5):5699-5708. doi: 10.1021/acsnano.4c15964. Epub 2025 Jan 30.
2
An RDL Modeling and Thermo-Mechanical Simulation Method of 2.5D/3D Advanced Package Considering the Layout Impact Based on Machine Learning.一种基于机器学习考虑布局影响的2.5D/3D先进封装的RDL建模与热机械仿真方法
Micromachines (Basel). 2023 Jul 30;14(8):1531. doi: 10.3390/mi14081531.
3
Investigation of Warpage for Multi-Die Fan-Out Wafer-Level Packaging Process.
多芯片扇出型晶圆级封装工艺的翘曲研究
Materials (Basel). 2022 Feb 23;15(5):1683. doi: 10.3390/ma15051683.