Solder Joint Reliability Assessment and Pad Size Studies of FO-WLP With Glass Substrate

Wang, P. H.; Lee, Y. C.; Lee, C. K.; Chang, Hsie-Chia; Chiang, Kuo‐Ning

doi:10.1109/tdmr.2021.3056054

Cited by 12 publications

(12 citation statements)

References 12 publications

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“…The empirical formula for Coffin–Manson equivalent plastic strain model is shown in Equation (1):

where

is mean cycle to failure, C and η are empirical constants, and

is the incremental equivalent plastic strain. For SAC solder joint,

and

are 0.235 and 1.75 [ 61 , 62 ].…”

Section: Finite Element Methods For Wlpmentioning

confidence: 99%

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An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

et al. 2021

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Several design parameters affect the reliability of wafer-level type advanced packaging, such as upper and lower pad sizes, solder volume, buffer layer thickness, and chip thickness, etc. Conventionally, the accelerated thermal cycling test (ATCT) is used to evaluate the reliability life of electronic packaging; however, optimizing the design parameters through ATCT is time-consuming and expensive, reducing the number of experiments becomes a critical issue. In recent years, many researchers have adopted the finite-element-based design-on-simulation (DoS) technology for the reliability assessment of electronic packaging. DoS technology can effectively shorten the design cycle, reduce costs, and effectively optimize the packaging structure. However, the simulation analysis results are highly dependent on the individual researcher and are usually inconsistent between them. Artificial intelligence (AI) can help researchers avoid the shortcomings of the human factor. This study demonstrates AI-assisted DoS technology by combining artificial intelligence and simulation technologies to predict wafer level package (WLP) reliability. In order to ensure reliability prediction accuracy, the simulation procedure was validated by several experiments prior to creating a large AI training database. This research studies several machine learning models, including artificial neural network (ANN), recurrent neural network (RNN), support vector regression (SVR), kernel ridge regression (KRR), K-nearest neighbor (KNN), and random forest (RF). These models are evaluated in this study based on prediction accuracy and CPU time consumption.

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“…The empirical formula for Coffin–Manson equivalent plastic strain model is shown in Equation (1):

where

is mean cycle to failure, C and η are empirical constants, and

is the incremental equivalent plastic strain. For SAC solder joint,

and

are 0.235 and 1.75 [ 61 , 62 ].…”

Section: Finite Element Methods For Wlpmentioning

confidence: 99%

“…where N f is mean cycle to failure, C and η are empirical constants, and ∆ε pl eq is the incremental equivalent plastic strain. For SAC solder joint, C and η are 0.235 and 1.75 [61,62].…”

Section: Finite Element Methods For Wlpmentioning

confidence: 99%

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

et al. 2021

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“…The average plastic strain per cycle was symbolized by PS . The fatigue exponent and the ductility coefficient are denoted by M and U , respectively 28 , 29 . …”

Section: Methodsmentioning

confidence: 99%

Reliability modeling of the fatigue life of lead-free solder joints at different testing temperatures and load levels using the Arrhenius model

Hani

Athamneh

Abueed

et al. 2023

Sci Rep

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Reliability of the microelectronic interconnection materials for electronic packages has a significant impact on the fatigue properties of the electronic assemblies. This is due to the correlation between solder joints reliability and the most frequent failure modes seen in electronic devices. Due to their superior mechanical and fatigue properties, SAC alloys have supplanted Pb-solder alloys as one of the most commonly used solder materials used as interconnection joints on electronic packages. The main aim of this study is to develop a prediction model of the fatigue life of the solder joints as a function of the experimental conditions. Using a customized experimental setup, an accelerated fatigue shear test is applied to examine the fatigue life of the individual SAC305 solder joints at actual setting conditions. OSP surface finish and solder mask defined are used in the studied test vehicle. The fatigue test includes three levels of stress amplitude and four levels of testing temperature. A two-parameter Weibull distribution is used for the reliability analysis for the fatigue life of the solder joints. A stress–strain curve is plotted for each cycle to construct the hysteresis loop at each cyclic load and testing temperature. The acquired hysteresis loop is used to estimate the inelastic work per cycle and plastic strain. The Morrow energy and Coffin Manson models are employed to describe the effects of the fatigue properties on the fatigue life of the solder joints. The Arrhenius model is implemented to illustrate the evolutions in the stress life, Morrow, and Coffin Manson equations at various testing temperatures. The fatigue life of SAC305 solder joints is then predicted using a general reliability model as a function of the stress amplitude and testing temperature.

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“…In the case of WLP, the thermal loading failure usually occurs at the outermost diagonal solder ball of the package. This study uses five WLP test vehicles (TV: WLP1-5) [ 16 , 17 , 18 ] and one fan-out WLP (FO-WLP, [ 19 ]) for FEA model validation. The structure component sizes, materials, and mean-time-to-failure (MTTF) reliability life are shown in Table 1 , Table 2 and Table 3 [ 17 , 18 , 19 ].…”

Section: Wlp Fea Model Validationmentioning

confidence: 99%

“…This study uses five WLP test vehicles (TV: WLP1-5) [ 16 , 17 , 18 ] and one fan-out WLP (FO-WLP, [ 19 ]) for FEA model validation. The structure component sizes, materials, and mean-time-to-failure (MTTF) reliability life are shown in Table 1 , Table 2 and Table 3 [ 17 , 18 , 19 ]. This research uses these data to verify our simulation results.…”

Section: Wlp Fea Model Validationmentioning

confidence: 99%

Predicting Wafer-Level Package Reliability Life Using Mixed Supervised and Unsupervised Machine Learning Algorithms

Chiang

2022

Materials

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With the increasing demand for electronic products, the electronic package gradually developed toward miniaturization and high density. The most significant advantage of the Wafer-Level Package (WLP) is that it can effectively reduce the volume and footprint area of the package. An important issue in the design of WLP is how to quickly and accurately predict the reliability life under the accelerated thermal cycling test (ATCT). If the simulation approach is not adopted, it usually takes several ACTCs to design a WLP, and each ACTC will take several months to get the reliability life results, which increases development time considerably. However, simulation results may differ depending on the designer’s domain knowledge, ability, and experience. This shortcoming can be overcome with artificial intelligence (AI). In this study, finite element analysis (FEA) is combined with machine learning algorithms, e.g., Kernel Ridge Regression (KRR), to create an AI model for predicting the reliability life of electronic packaging. Kernel Ridge Regression (KRR) combined with the K-means cluster algorithm provides a highly accurate and efficient way to obtain AI models for large-scale data sets.

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Solder Joint Reliability Assessment and Pad Size Studies of FO-WLP With Glass Substrate

Cited by 12 publications

References 12 publications

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

Reliability modeling of the fatigue life of lead-free solder joints at different testing temperatures and load levels using the Arrhenius model

Predicting Wafer-Level Package Reliability Life Using Mixed Supervised and Unsupervised Machine Learning Algorithms

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