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This study aims to investigate the thermal fracture mechanism of moisture-preconditioned SAC305 ball grid array (BGA) solder joints subjected to multiple reflow and thermal cycling.

The BGA package samples are subjected to JEDEC Level 1 accelerated moisture treatment (85 °C/85%RH/168 h) with five times reflow at 270 °C. This is followed by multiple thermal cycling from 0 °C to 100 °C for 40 min per cycle, per IPC-7351B standards. For fracture investigation, the cross-sections of the samples are examined and analysed using the dye-and-pry technique and backscattered scanning electron microscopy. The packages' microstructures are characterized using an energy-dispersive X-ray spectroscopy approach. Also, the package assembly is investigated using the Darveaux numerical simulation method.

The study found that critical strain density is exhibited at the component pad/solder interface of the solder joint located at the most distant point from the axes of symmetry of the package assembly. The fracture mechanism is a crack fracture formed at the solder's exterior edges and grows across the joint's transverse section. It was established that Au content in the formed intermetallic compound greatly impacts fracture growth in the solder joint interface, with a composition above 5 Wt.% Au regarded as an unsafe level for reliability. The elongation of the crack is aided by the brittle nature of the Au-Sn interface through which the crack propagates. It is inferred that refining the solder matrix elemental compound can strengthen and improve the reliability of solder joints.

Inspection lead time and additional manufacturing expenses spent on investigating reliability issues in BGA solder joints can be reduced using the study's findings on understanding the solder joint fracture mechanism.

Limited studies exist on the thermal fracture mechanism of moisture-preconditioned BGA solder joints exposed to both multiple reflow and thermal cycling. This study applied both numerical and experimental techniques to examine the reliability issue.

The purpose of this study is to investigate the effects of Ce and Sb doping on the microstructure and thermal mechanical properties of Sn-1.0Ag-0.5Cu lead-free solder. The effects of 0.5%Sb and 0.07%Ce doping on microstructure, thermal properties and mechanical properties of Sn-1.0Ag-0.5Cu lead-free solder were investigated.

According to the mass ratio, the solder alloys were prepared from tin ingot, antimony ingot, silver ingot and copper ingot with purity of 99.99% at 400°C. X-ray diffractometer was adopted for phase analysis of the alloys. Optical microscopy, scanning electron microscopy and energy dispersive spectrometer were used to study the effect of the Sb and Ce doping on the microstructure of the solder. Then, the thermal characteristics of alloys were characterized by a differential scanning calorimeter (DSC). Finally, the ultimate tensile strength (UTS), elongation (EL.%) and yield strength (YS) of solder alloys were measured by tensile testing machine.

With the addition of Sb and Ce, the ß-Sn and intermetallic compounds of solders were refined and distributed more evenly. With the addition of Sb, the UTS, EL.% and YS of Sn-1.0Ag-0.5Cu increased by 15.3%, 46.8% and 16.5%, respectively. The EL.% of Sn-1.0Ag-0.5Cu increased by 56.5% due to Ce doping. When both Sb and Ce elements are added, the EL.% of Sn-1.0Ag-0.5Cu increased by 93.3%.

The addition of 0.5% Sb and 0.07% Ce can obtain better comprehensive performance, which provides a helpful reference for the development of Sn-Ag-Cu lead-free solder.

This study aims to characterize the mechanical properties of sintered nano-silver under various sintering processes by nano-indentation tests.

Through microstructure observations and characterization, the influences of sintering process on the microstructure evolutions of sintered nano-silver were presented. And, the indentation load, indentation displacement curves of sintered silver under various sintering processes were measured by using nano-indentation test. Based on the nano-indentation test, a reverse analysis of the finite element calculation was used to determine the yielding stress and hardening exponent.

The porosity decreases with the increase of the sintering temperature, while the average particle size of sintered nano-silver increases with the increase of sintering temperature and sintering time. In addition, the porosity reduced from 34.88%, 30.52%, to 25.04% if the ramp rate was decreased from 25°C/min, 15°C/min, to 5°C/min, respectively. The particle size appears more frequently within 1 µm and 2 µm under the lower ramp rate. With reverse analysis, the strain hardening exponent gradually heightened with the increase of temperature, while the yielding stress value decreased significantly with the increase of temperature. When the sintering time increased, the strain hardening exponent increased slightly.

The mechanical properties of sintered nano-silver under different sintering processes are clearly understood.

This paper could provide a novel perspective on understanding the sintering process effects on the mechanical properties of sintered nano-silver.

This study aims to focus on the surface mount technology (SMT) mass production process of Sn-9Zn-2.5Bi-1.5In solder. It explores it with some components that will provide theoretical support for the industrial SMT application of Sn-Zn solder.

This study evaluates the properties of solder pastes and selects a more appropriate reflow parameter by comparing the microstructure of solder joints with different reflow soldering profile parameters. The aim is to provide an economical and reliable process for SMT production in the industry.

Solder paste wettability and solder ball testing in a nitrogen environment with an oxygen content of 3,000 ppm meet the requirements of industrial production. The printing performance of the solder paste is good and can achieve a printing rate of 100–160 mm/s. When soldering with a traditional stepped reflow soldering profile, air bubbles are generated on the surface of the solder joint, and there are many voids and defects in the solder joint. A linear reflow soldering profile reduces the residence time below the melting point of the solder paste (approximately 110 s). This reduces the time the zinc is oxidized, reducing solder joint defects. The joint strength of tin-zinc joints soldered with the optimized reflow parameters is close to that of Sn-58Bi and SAC305, with high joint strength.

This study attempts to industrialize the application of Sn-Zn solder and solves the problem that Sn-Zn solder paste is prone to be oxidized in the application and obtains the SMT process parameters suitable for Sn-9Zn-2.5Bi-1.5In solder.

This study aims to investigate the possible defects and their root causes in a soft-termination multilayered ceramic capacitor (MLCC) when subjected to a thermal reflow process.

Specimens of the capacitor assembly were subjected to JEDEC level 1 preconditioning (85 °C/85%RH/168 h) with 5× reflow at 270°C peak temperature. Then, they were inspected using a 2 µm scanning electron microscope to investigate the evidence of defects. The reliability test was also numerically simulated and analyzed using the extended finite element method implemented in ABAQUS.

Excellent agreements were observed between the SEM inspections and the simulation results. The findings showed evidence of discontinuities along the Cu and the Cu-epoxy layers and interfacial delamination crack at the Cu/Cu-epoxy interface. The possible root causes are thermal mismatch between the Cu and Cu-epoxy layers, moisture contamination and weak Cu/Cu-epoxy interface. The maximum crack length observed in the experimentally reflowed capacitor was measured as 75 µm, a 2.59% difference compared to the numerical prediction of 77.2 µm.

This work's contribution is expected to reduce the additional manufacturing cost and lead time in investigating reliability issues in MLCCs.

Despite the significant number of works on the reliability assessment of surface mount capacitors, work on crack growth in soft-termination MLCC is limited. Also, the combined experimental and numerical investigation of reflow-induced reliability issues in soft-termination MLCC is limited. These cited gaps are the novelties of this study.

The purpose of this paper was to investigate how variations in the geometry of silicon chips and the presence of surface defects affect their static bending properties. By comparing the bending radius and strength across differently sized and treated chips, the study sought to understand the underlying mechanics that contribute to the flexibility of silicon-based electronic devices. This understanding is crucial for the development of advanced, robust and adaptable electronic systems that can withstand the rigors of manufacturing and everyday use.

This study explores the impact of silicon chip geometry and surface defects on flexibility through a multifaceted experimental approach. The methodology included preparing silicon chips of three distinct dimensions and subjecting them to thinning processes to achieve a uniform thickness verified via scanning electron microscopy (SEM). Finite element method (FEM) simulations and a series of four-point bending tests were used to analyze the bending flexibility theoretically and experimentally. The approach was comprehensive, examining both the intrinsic geometric factors and the extrinsic influence of surface defects induced by manufacturing processes.

The findings revealed a significant deviation between the theoretical predictions from FEM simulations and the experimental outcomes from the four-point bending tests. Rectangular-shaped chips demonstrated superior flexibility, with smaller dimensions leading to an increased bending strength. Surface defects, identified as critical factors affecting flexibility, were analyzed through SEM and atomic force microscopy, showing that etching processes could reduce defect density and enhance flexibility. Notably, the study concluded that surface defects have a more pronounced impact on silicon chip flexibility than geometric factors, challenging initial assumptions and highlighting the need for defect minimization in chip manufacturing.

This research contributes valuable insights into the design and fabrication of flexible electronic devices, emphasizing the significant role of surface defects over geometric considerations in determining silicon chip flexibility. The originality of the work lies in its holistic approach to dissecting the factors influencing silicon chip flexibility, combining theoretical simulations with practical bending tests and surface defect analysis. The findings underscore the importance of optimizing manufacturing processes to reduce surface defects, thereby paving the way for the creation of more durable and flexible electronic devices for future technologies.

Adoption of Clinical Decision Support Systems (CDSS) is a crucial step towards the digital transition of the healthcare sector. This review aims to determine and synthesise the influential factors in CDSS adoption in inpatient healthcare settings in order to grasp an understanding of the phenomenon and identify future research gaps.

A systematic literature search of five databases (Medline, EMBASE, PsycINFO, Web of Science and Scopus) was conducted between January 2010 and June 2023. The search strategy was a combination of the following keywords and their synonyms: clinical decision support, hospital or secondary care and influential factors. The quality of studies was evaluated against a 40-point rating scale.

Thirteen papers were systematically reviewed and synthesised and deductively classified into three main constructs of the Technology–Organisation–Environment theory. Scarcity of papers investigating CDSS adoption and its challenges, especially in developing countries, was evident.

This study offers a summative account of challenges in the CDSS procurement process. Strategies to help adopters proactively address the challenges are: (1) Hospital leaders need a clear digital strategy aligned with stakeholders' consensus; (2) Developing modular IT solutions and conducting situational analysis to achieve IT goals; and (3) Government policies, accreditation standards and procurement guidelines play a crucial role in navigating the complex CDSS market.

To the best of the authors’ knowledge, this is the first review to address the adoption and procurement of CDSS. Previous literature only addressed challenges and facilitators within the implementation and post-implementation stages. This study focuses on the firm-level adoption phase of CDSS technology with a theory refining lens.

Recently, powerful instruments for biomedical engineering research studies, including disease modeling, drug designing and nano-drug delivering, have been extremely investigated by researchers. Particularly, investigation in various microfluidics techniques and novel biomedical approaches for microfluidic-based substrate have progressed in recent years, and therefore, various cell culture platforms have been manufactured for these types of approaches. These microinstruments, known as tissue chip platforms, mimic in vivo living tissue and exhibit more physiologically similar vitro models of human tissues. Using lab-on-a-chip technologies in vitro cell culturing quickly caused in optimized systems of tissues compared to static culture. These chipsets prepare cell culture media to mimic physiological reactions and behaviors.

The authors used the application of lab chip instruments as a versatile tool for point of health-care (PHC) applications, and the authors applied a current progress in various platforms toward biochip DNA sensors as an alternative to the general bio electrochemical sensors. Basically, optical sensing is related to the intercalation between glass surfaces containing biomolecules with fluorescence and, subsequently, its reflected light that arises from the characteristics of the chemical agents. Recently, various techniques using optical fiber have progressed significantly, and researchers apply highlighted remarks and future perspectives of these kinds of platforms for PHC applications.

The authors assembled several microfluidic chips through cell culture and immune-fluorescent, as well as using microscopy measurement and image analysis for RNA sequencing. By this work, several chip assemblies were fabricated, and the application of the fluidic routing mechanism enables us to provide chip-to-chip communication with a variety of tissue-on-a-chip. By lab-on-a-chip techniques, the authors exhibited that coating the cell membrane via poly-dopamine and collagen was the best cell membrane coating due to the monolayer growth and differentiation of the cell types during the differentiation period. The authors found the artificial membrane, through coating with Collagen-A, has improved the growth of mouse podocytes cells-5 compared with the fibronectin-coated membrane.

The authors could distinguish the differences across the patient cohort when they used a collagen-coated microfluidic chip. For instance, von Willebrand factor, a blood glycoprotein that promotes hemostasis, can be identified and measured through these type-coated microfluidic chips.

Education 4.0 (E 4.0) represents a new paradigm in the field of education, which emphasizes a student-centric approach that allows learners to access education anytime, anywhere, tailored to their individual needs through modern-day technologies. The purpose of the study was to unearth the critical success factors (CSFs) essential for the successful implementation of E 4.0.

The CSFs were unearthed using a literature review and further the interrelationships were analysed using multi-criteria decision making (MCDM) approach.

The study unearthed 15 CSFs for the successful implementation of E 4.0. The most important factor for the successful implementation of E 4.0 was personalized learning which was found to be the casual factor. The other causal CSFs were clear vision and leadership for E 4.0, stakeholder involvement, data analytics in teaching and learning, inter-disciplinary learning and blended learning environments. The effect factors were digital citizenship-based education, teacher training and development for E 4.0, supportive environment, curriculum redesign for E 4.0, open educational resources, digital technologies, formative assessments, infrastructure for E 4.0 and sustainability in education.

This is the first study which unearthed the CSFs and found the interrelationships among them, thus contributing to the theory of technology organization environment.

This study represented a pioneering effort in understanding the CSFs underpinning the successful adoption of E 4.0, paving the way for a more personalized, tech-savvy and effective education system.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.