Thermal fatigue life prediction of solder joints of plastic ball grid array packages

Chu, Yin Fun; Yi, Sung; Geng, P.

doi:10.1504/ijmsi.2014.064769

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…The roles of solders are to make an electrical connection as well as to protect solder joints from external mechanical shock or external chemical environment. However, when solder joints are under thermal cyclic loading, mismatch in the coefficient of thermal expansions (CTE) between PCB and solder balls creates thermal strains and stresses on the joints, which may finally result in cracking (Zhong and Yi (1999), Zhong et al (2000); Zhong et al (2002), Kim et al (2009); Chu et al (2014), Yao et al (2019)). Consequently, the mechanical interconnection is lost, leading to electrical failures (such as hard/intermittent open and parametric failure), which in turn causes malfunction of the circuit or whole system.…”

Section: Introductionmentioning

confidence: 99%

Machine learning framework for predicting reliability of solder joints

Jones

2019

SSMT

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Purpose This paper aims to present a machine learning framework for using big data analytics to predict the reliability of solder joints. The purpose of this study is to accurately predict the reliability of solder joints by using big data analytics. Design/methodology/approach A machine learning framework for using big data analytics is proposed to predict the reliability of solder joints accurately. Findings A machine learning framework for predicting the life of solder joints accurately has been developed in this study. To validate its accuracy and efficiency, it is applied to predict the long-term reliability of lead-free Sn96.5Ag3.0Cu0.5 (SAC305) for three commonly used surface finishes such OSP, ENIG and IAg. The obtained results show that the predicted failure based on the machine learning method is much more accurate than the Weibull method. In addition, solder ball/bump joint failure modes are identified based on various solder joint failures reported in the literature. Originality/value The ability to predict thermal fatigue life accurately is extremely valuable to the industry because it saves time and cost for product development and optimization.

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Section: Introductionmentioning

confidence: 99%

Machine learning framework for predicting reliability of solder joints

Jones

2019

SSMT

Self Cite

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“…Meanwhile, the electronic components have a low thermal strength and would not be suitable for the reflow process. Moreover, a high current density recently caused thermal cracks 12–16 in the connecting part of the electric circuits, which are mainly caused by power devices 10,17 because of the low thermal conductivity of the connecting part. Therefore, the electronic components lead to failure because the heat is confined.…”

Section: Introductionmentioning

confidence: 99%

A 64-pin Nanowire Surface Fastener Like a Ball Grid Array Applied for Room-temperature Electrical Bonding

Toku

Ichioka

Morita

et al. 2019

Sci Rep

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Surface-mount techniques primarily depend on soldering. However, soldering techniques have encountered some challenges in recent years. These challenges include rare metal recycling, thermal problems, and Pb toxicity. We recently developed a metallic nanowire surface fastener (NSF) to resolve the abovementioned problems. This fastener can be used to connect electronic components on a substrate at room temperature using the van der Waals force between each nanowire. This study demonstrates a 64-pin NSF that behaves like a ball grid array (BGA) for application to actual electronic devices. The adhesion strength and electrical properties of the NSF were investigated by adjusting the nanowire parameters, such as diameter, length, density (number per area), preload, and shape. The shape control of the nanowires greatly contributed to the improvement of the properties. A maximum adhesion strength of 16.4 N/cm2 was achieved using a bent, hook-like NSF. This strength was 4–5 times the value of the straight NSF. The contact resistivity was 2.98 × 10−2 Ω∙cm2. The NSF fabricated through the simple template method showed the room temperature bonding ability and adaptability to a highly ordered electrode like the BGA.

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Stress Analysis for Chip Scale Packages with Embedded Active Devices under Thermal Cycling

Yeo¹

2000

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Thermal fatigue life prediction of solder joints of plastic ball grid array packages

Cited by 3 publications

References 10 publications

Machine learning framework for predicting reliability of solder joints

Machine learning framework for predicting reliability of solder joints

A 64-pin Nanowire Surface Fastener Like a Ball Grid Array Applied for Room-temperature Electrical Bonding

Stress Analysis for Chip Scale Packages with Embedded Active Devices under Thermal Cycling

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