Comprehensive Study on 2.5D Package Design for Board-Level Reliability in Thermal Cycling and Power Cycling

Shao, Shuyan; Niu, Yuling; Wang, Jing; Rui-yang, Liu; Park, Seungbae; Lee, Hohyung; Refai-Ahmed, Gamal; Yip, Laurene

doi:10.1109/ectc.2018.00251

Cited by 37 publications

(4 citation statements)

References 34 publications

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“…If the temperature distribution inside the chip package is not uniform, the signal transmission characteristics of the chip will be influenced, thereby affecting the life and reliability of the electronic device. Additionally, because of the differences in the thermal expansion coefficient between chip packaging materials [31][32][33][34][35][36][37][38][39][40][41][42][43][44], different materials have different degrees of thermal expansion and contraction under the influence of temperature. This results in additional stress and strain inside the electronic components, especially inside solder joints.…”

Section: Fatigue Failure Factors Of Bga Solder Jointsmentioning

confidence: 99%

Survey on Fatigue Life Prediction of BGA Solder Joints

et al. 2022

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With the development of science and technology, consumers’ requirements for various electronic devices present a trend of more diverse functions and thinner bodies. This makes integrated circuits mounted in electronic products and their packaging more vital to satisfying the above requirements. Ball grid array (BGA) packaging is widely used in the field of microelectronic manufacturing industries due to its multiple I/O volumes and excellent electric characteristics. However, due to environmental loads such as vibration and impact during its production and application, defects inevitably emerge in BGA solder joint defects, which will lead to the failure of electronic products. This article summarizes the state-of-the-art research on the factors, analysis methods, and models for the fatigue failure of BGA chips. After rigorous discussions concerning this research, some theoretical suggestions are provided for BGA packaging in reliability analysis and the establishment of evaluation standards.

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Section: Fatigue Failure Factors Of Bga Solder Jointsmentioning

confidence: 99%

Survey on Fatigue Life Prediction of BGA Solder Joints

et al. 2022

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“…Lai used the computational fluid dynamics (CFD) model to analyze the temperatures of solder balls of a BGA assembly during the reflow soldering process (Lai et al, 2021). Shao used The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/0954-0911.htm the CFD model to obtain the working status temperature distribution of a 2.5 D package and mapped it to the finite element analysis model to observe component warpage (Shao et al, 2018). Deng used the CFD model to predict the temperature distribution of a system in package assembly during the reflow soldering process (Deng et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

An intelligent system for reflow oven temperature settings based on hybrid physics-machine learning model

Lai

Pan

Cen

et al. 2022

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Purpose This paper aims to provide the proper preset temperatures of the convection reflow oven when reflowing a printed circuit board (PCB) assembly with varied sizes of components simultaneously. Design/methodology/approach In this study, computational fluid dynamics modeling is used to simulate the reflow soldering process. The training data provided to the machine learning (ML) model is generated from a programmed system based on the physics model. Support vector regression and an artificial neural network are used to validate the accuracy of ML models. Findings Integrated physical and ML models synergistically can accurately predict reflow profiles of solder joints and alleviate the expense of repeated trials. Using this system, the reflow oven temperature settings to achieve the desired reflow profile can be obtained at substantially reduced computation cost. Practical implications The prediction of the reflow profile subjected to varied temperature settings of the reflow oven is beneficial to process engineers when reflowing bulky components. The study of reflowing a new PCB assembly can be started at the early stage of board design with no need for a physical profiling board prototype. Originality/value This study provides a smart solution to determine the optimal preset temperatures of the reflow oven, which is usually relied on experience. The hybrid physics–ML model providing accurate prediction with the significantly reduced expense is used in this application for the first time.

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“…The authors proved that maximum warpage and the remaining residual warpage in the components can be predicted accurately. Shao et al (2018) studied a PCB model with 2.5D packages to determine solder fatigue life. The authors simulated several parameters to improve power-cycling life prediction for and the temperature results of the finite element model.…”

Section: Introductionmentioning

confidence: 99%

Dynamic warpage simulation of molded PCB under reflow process

Sek

Abdullah

et al. 2021

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Purpose This study aims to simulate molded printed circuit board (PCB) warpage behavior under reflow temperature distribution. Simulation models are used to estimate dynamic warpage behavior for different form factor sizes. Design/methodology/approach This study analyzes warpage during the reflow process. The shadow moiré experiment methodology is used to collect data on the dynamic warpage performance of a model with a form factor of 10mm × 10mm × 1mm. The temperature profile with heating from 25°C to 300°C at intervals of 50°C is used, and the sample is made to undergo a cooling process until it reaches the room temperature. Subsequently, ANSYS static structural simulation is performed on similar form factor models to ascertain the accuracy of the simulation results. Findings Results show that the deformation and total force induced by coefficient of thermal expansion (CTE) mismatch are examined based on the warpage performance of models with different sizes, that is, 45mm × 45mm × 1mm and 45mm × 15mm × 1mm. Compared with the experimental data, the simulated modeling accuracy yields a less than 5% deviation in the dynamic warpage prediction at a reflow temperature of 300°C. Results also reveal that the larger the model, the larger the warpage changes under the reflow temperature. Research limitations/implications The simulated warpage is limited to the temperature and force induced by CTE mismatch between two materials. The form factor of the ball-grid array model is limited to only three different sizes. The model is assumed to be steady, isothermal and static. The simulation adopts homogenous materials, as it cannot accurately model nonhomogeneous multilayered composite materials. Practical implications This study can provide engineers and researchers with a profound understanding of molded PCB warpage, minimal resource utilization and the improved product development process. Social implications The accurate prediction of molded PCB warpage can enable efficient product development and reduce resources and production time, thereby creating a sustainable environment. Originality/value The literature review points out that warpage in various types of PCBs was successfully examined, and that considerable efforts were exerted to investigate warpage reduction in PCB modules. However, PCB warpage studies are limited to bare PCBs. To the best of the authors’ knowledge, the examination of warpage in a molded PCB designed with a molded compound cover, as depicted in Figure 3, is yet to be conducted. A molded compound provides strong lattice support for PCBs to prevent deformation during the reflow process, which is a topic of considerable interest and should be explored.

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Comprehensive Study on 2.5D Package Design for Board-Level Reliability in Thermal Cycling and Power Cycling

Cited by 37 publications

References 34 publications

Survey on Fatigue Life Prediction of BGA Solder Joints

Survey on Fatigue Life Prediction of BGA Solder Joints

An intelligent system for reflow oven temperature settings based on hybrid physics-machine learning model

Dynamic warpage simulation of molded PCB under reflow process

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