Role of aging temperature on thermomechanical fatigue lifetime of solder joints in electronic systems

Siswanto, Waluyo Adi; Borodin, Kirill; Mahmoud, Zaid H.; Surendar, A.; Sajjadifar, Sami; Abdilova, Galiya; Chang, Jei Wen

doi:10.1108/ssmt-07-2020-0029

Cited by 4 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Siswanto et al used the Morrow energy model to construct a prediction model for the fatigue life of barrel-type solder joints at different aging temperatures. A negative relationship between aging temperature and solder life was found in the study, where a modified estimation equation was used to illustrate this relationship 9 . Tsou et al explored the impacts of the strain rate and creep effect that were initiated due to the thermal cycling process by using a finite element model (FEM) simulation.…”

Section: Introductionmentioning

confidence: 80%

Neural-fuzzy machine learning approach for the fatigue-creep reliability modeling of SAC305 solder joints

Hani

Athamneh

Abueed

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The accuracy of reliability models is one of the most problematic issues that must be considered for the life of electronic assemblies, particularly those used for critical applications. The reliability of electronics is limited by the fatigue life of interconnected solder materials, which is influenced by many factors. This paper provides a method to build a robust machine-learning reliability model to predict the life of solder joints in common applications. The impacts of combined fatigue and creep stresses on solder joints are also investigated in this paper. The common alloy used in solder joint fabrication is SAC305 (Sn–Ag–Cu). The test vehicle includes individual solder joints of SAC305 alloy assembled on a printed circuit board. The effects of testing temperature, stress amplitude, and creep dwell time on the life of solder joints were considered. A two-parameter Weibull distribution was utilized to analyze the fatigue life. Inelastic work and plastic strain were extracted from the stress–strain curves. Then, Artificial Neural Networks (ANNs) were used to build a machine learning model to predict characteristic life obtained from the Weibull analysis. The inelastic work and plastic stains were also considered in the ANN model. Fuzzy logic was used to combine the process parameters and fatigue properties and to construct the final life prediction model. Then a relationship equation between the comprehensive output measure obtained from the fuzzy system and the life was determined using a nonlinear optimizer. The results indicated that increasing the stress level, testing temperature, and creep dwell time decreases reliability. The case of long creep dwell time at elevated temperatures is worst in terms of impact on reliability. Finally, a single robust reliability model was computed as a function of the fatigue properties and process parameters. A significant enhancement of the prediction model was achieved compared to the stress–life equations.

show abstract

Section: Introductionmentioning

confidence: 80%

Neural-fuzzy machine learning approach for the fatigue-creep reliability modeling of SAC305 solder joints

Hani

Athamneh

Abueed

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…At least four main antimicrobial agents can be considered for this group (Fatla et al, 2015;Liu et al, 2022;Sallal et al, 2021;Siswanto et al, 2021) • Inorganic ion exchange materials: Intermediate metal salts such as silver and copper with mineral carriers (zeolites) used in synthetic fibers. By controlling the release of silver ions by a suitable mineral carrier, the antimicrobial properties and possible negative effects on the fiber are controlled.…”

Section: Modified Chemical Compounds With Antimicrobial Effectmentioning

confidence: 99%

Antimicrobial finishing of textiles using nanomaterials

Abdul-Reda Hussein,

Mahmoud,

Alaziz

et al. 2024

Braz. J. Biol.

View full text Add to dashboard Cite

Metals, such as copper and silver, can be extremely toxic to bacteria at exceptionally low concentrations. Because of this biocidal activity, metals have been widely used as antimicrobial agents in a multitude of applications related with agriculture, healthcare, and the industry in general. A large number of microorganisms live in the human environment. if the balance of these creatures in nature is disturbed, the health of the individual and society will be threatened due to the production and emission of unpleasant odors and the reduction of health standards. The presence of microorganisms on textiles can cause adverse effects such as discoloration or staining on textiles, decomposition of fibrous materials, reduced strength, and eventually decay of textiles. Most fibers and polymers do not show resistance to the effects of microbes and by providing growth factors for microorganisms such as the right temperature and humidity, nutrients from sweat and fat from skin glands, dead skin cells as well as materials used in the stage of finishing the textiles causes the rapid growth and spread of various microbes. With the advent of nanotechnology, various industries and human daily life underwent changes. In recent years, increasing research on nanoparticles has led to the production of textiles with greater efficiency and added value. These modified textiles prevent the spread of unpleasant odors, the spread, and transmission of diseases. This article reviews the basics and principles of antimicrobial tetiles, as well as a brief overview of antimicrobial materials and nanostructures with antimicrobial properties.

show abstract

Creep–fatigue lifetime estimation of SnAgCu solder joints using an artificial neural network approach

Chen¹,

Zhu

Jiao

et al. 2021

Mechanics of Advanced Materials and Structures

View full text Add to dashboard Cite

Role of aging temperature on thermomechanical fatigue lifetime of solder joints in electronic systems

Cited by 4 publications

References 38 publications

Neural-fuzzy machine learning approach for the fatigue-creep reliability modeling of SAC305 solder joints

Neural-fuzzy machine learning approach for the fatigue-creep reliability modeling of SAC305 solder joints

Antimicrobial finishing of textiles using nanomaterials

Creep–fatigue lifetime estimation of SnAgCu solder joints using an artificial neural network approach

Contact Info

Product

Resources

About