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This paper aims to study the relationship between the ball grid array (BGA) flip-chip underfilling process parameter and its void formation region.

A set of top-down scanning acoustic microscope images of BGA underfill is collected and void labelled. The labelled images are trained with a convolutional neural network model, and the performance is evaluated. The model is tested with new images, and the void area with its region is analysed with its dispensing parameter.

All findings were well-validated with reference to the past experimental results regarding dispensing parameters and their quantitative regional formation. As the BGA is non-uniform, 85% of the test samples have void(s) formed in the emptier region. Furthermore, the highest rating factor, valve dispensing pressure with a Gini index of 0.219 and U-type dispensing pattern set of parameters generally form a lower void percentage within the underfilling, although its consistency is difficult to maintain.

This study enabled manufacturers to forecast the void regional formation from its filling parameters and array pattern. The filling pressure, dispensing pattern and BGA relations could provide qualitative insights to understand the void formation region in a flip-chip, enabling the prompt to formulate countermeasures to optimise voiding in a specific area in the underfill.

The void regional formation in a flip-chip underfilling process can be explained quantitatively with indicative parameters such as valve pressure, dispensing pattern and BGA arrangement.

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.

Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.

The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

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.

This study aims to investigate the reliability issues of microvoid cracks in solder joint packages exposed to thermal cycling fatigue.

The specimens are subjected to JEDEC preconditioning level 1 (85 °C/85%RH/168 h) with five times reflow at 270°C. This is followed by thermal cycling from 0°C to 100°C, per IPC-7351B standards. The specimens' cross-sections are inspected for crack growth and propagation under backscattered scanning electronic microscopy. The decoupled thermomechanical simulation technique is applied to investigate the thermal fatigue behavior. The impacts of crack length on the stress and fatigue behavior of the package are investigated.

Cracks are initiated from the ball grid array corner of the solder joint, propagating through the transverse section of the solder ball. The crack growth increases continuously up to 0.25-mm crack length, then slows down afterward. The J-integral and stress intensity factor (SIF) values at the crack tip decrease with increased crack length. Before 0.15-mm crack length, J-integral and SIF reduce slightly with crack length and are comparatively higher, resulting in a rapid increase in crack mouth opening displacement (CMOD). Beyond 0.25-mm crack length, the values significantly decline, that there is not much possibility of crack growth, resulting in a negligible change in CMOD value. This explains the crack growth arrest obtained after 0.25-mm crack length.

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

The work investigates crack propagation mechanisms of microvoid cracks in solder joints exposed to moisture and thermal fatigue, which is still limited in the literature. The parametric variation of the crack length on stress and fatigue characteristics of solder joints, which has never been conducted, is also studied.

Au stud bump bonding technology is an effective means to realize heterogeneous integration of commercial chips in the 2.5D electronic packaging. The purpose of this paper is to study the long-term reliability of the Au stud bump treated by four different high temperature storage times (200°C for 0, 100, 200 and 300 h).

The bonding strength and the fracture behavior are investigated by chip shear test. The experiment is further studied by microstructural characterization approaches such as scanning electron microscope, energy dispersive spectrometer and so on.

It is recognized that there were mainly three typical fracture models during the chip shear test among all the Au stud bump samples treated by high temperature storage. For solder bump before aging, the fracture occurred at the interface between the Cu pad and the Au stud bump. As the aging time increased, the fracture mainly occurred inside the Au stud bump at 200°C for 100 and 200 h. When aging time increased to 300 h, it is found that the fracture transferred to the interface between the Au stud bump and the Al Pad.

In addition, the bonding strength also changed with the high temperature storage time increasing. The bonding strength does not change linearly with the high temperature storage time increasing but decreases first and then increases. The investigation shows that the formation of the intermetallic compounds because of the reaction between the Au and Al atoms plays a key role on the bonding strength and fracture behavior variation.