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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

An RDL Modeling and Thermo-Mechanical Simulation Method of 2.5D/3D Advanced Package Considering the Layout Impact Based on Machine Learning.

作者信息

Wu Xiaodong, Wang Zhizhen, Ma Shenglin, Chu Xianglong, Li Chunlei, Wang Wei, Jin Yufeng, Wu Daowei

机构信息

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

School of Integrated Circuits, Peking University, Beijing 100871, China.

出版信息

Micromachines (Basel). 2023 Jul 30;14(8):1531. doi: 10.3390/mi14081531.

DOI:10.3390/mi14081531
PMID:37630067
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10456910/
Abstract

The decreasing-width, increasing-aspect-ratio RDL presents significant challenges to the design for reliability (DFR) of an advanced package. Therefore, this paper proposes an ML-based RDL modeling and simulation method. In the method, RDL was divided into blocks and subdivided into pixels of metal percentage, and the RDL was digitalized as tensors. Then, an ANN-based surrogate model was built and trained using a subset of tensors to predict the equivalent material properties of each block. Lastly, all blocks were transformed into elements for simulations. For validation, line bending simulations were conducted on an RDL, with the reaction force as an accuracy indicator. The results show that neglecting layout impact caused critical errors as the substrate thinned. According to the method, the reaction force error was 2.81% and the layout impact could be accurately considered with 200 × 200 elements. For application, the TCT maximum temperature state simulation was conducted on a CPU chip. The simulation indicated that for an advanced package, the maximum stress was more likely to occur in RDL rather than in bumps; both RDL and bumps were critically impacted by layouts, and RDL stress was also impacted by vias/bumps. The proposed method precisely concerned layout impacts with few resources, presenting an opportunity for efficient improvement.

摘要

宽度递减、纵横比递增的RDL给先进封装的可靠性设计(DFR)带来了重大挑战。因此,本文提出了一种基于机器学习的RDL建模与仿真方法。在该方法中,RDL被划分为多个块,并进一步细分为金属百分比像素,RDL被数字化为张量。然后,使用张量子集构建并训练基于人工神经网络的替代模型,以预测每个块的等效材料属性。最后,将所有块转换为用于仿真的单元。为进行验证,在一个RDL上进行了线弯曲仿真,将反作用力作为精度指标。结果表明,随着基板变薄,忽略布局影响会导致严重误差。根据该方法,反作用力误差为2.81%,使用200×200个单元可以准确考虑布局影响。在应用方面,对一个CPU芯片进行了TCT最高温度状态仿真。仿真表明,对于先进封装,最大应力更有可能出现在RDL而非凸块中;RDL和凸块均受到布局的严重影响,RDL应力还受到过孔/凸块的影响。所提出的方法以较少的资源精确考虑了布局影响,为高效改进提供了机会。

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