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A Model of BGA Thermal Fatigue Life Prediction Considering Load Sequence Effects
Weiwei Hu1,2, Yaqiu Li3, Yufeng Sun4
1Reliability and System Engineering School, Beihang University, Haidian District, Beijing 100191, China. hww@buaa.edu.cn.
Materials (Basel, Switzerland)
|August 5, 2017
Summary
This study introduces a new thermal fatigue life prediction model for ball grid array packages. It accounts for load sequence effects and improves testing data accessibility for microelectronic devices.
Area of Science:
- Materials Science
- Mechanical Engineering
- Reliability Engineering
Background:
- Accurate fatigue life prediction is crucial for microelectronic devices, but testing data is often limited.
- Existing models frequently overlook the impact of load cycle sequence on fatigue damage.
- Linear damage accumulation rules are common but do not capture sequence-dependent fatigue behavior.
Purpose of the Study:
- To propose a novel thermal fatigue life prediction model for ball grid array (BGA) packages.
- To incorporate the significant effects of load sequence into fatigue life predictions.
- To enhance the availability and accessibility of essential testing data for microelectronic components.
Main Methods:
- Development of a new thermal fatigue life prediction model specifically for BGA packages.
- Inclusion of load sequence effects within the fatigue damage assessment.
- Discussion and verification of a new failure criterion using simulation and experimental data.
Main Results:
- The proposed model effectively considers the influence of load sequence on thermal fatigue life.
- A new failure criterion was validated, improving data availability for microelectronic device testing.
- The study demonstrates the consequences of load sequence on fatigue under various conditions.
Conclusions:
- Load sequence effects are critical and must be considered in accurate fatigue life prediction for BGA packages.
- The developed model and failure criterion offer improved methods for assessing microelectronic device reliability.
- This research contributes to more robust fatigue life prediction methodologies by addressing data limitations and sequence dependency.
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