Solder Defects Research Articles

The Need for a New Approach to Soldering in High Pressure Die Casting

Soldering remains one of the most common defects in high pressure die casting (HPDC). It is most simply defined as aluminum stuck to the die surface. At ejection, the stuck aluminum gouges the casting resulting in dimensional and leaking defects. For nearly 50 years, a thermodynamics and kinetics understanding of soldering has been accepted while explicitly assuming that other mechanisms are not important. It will be shown that this thermodynamics and kinetics modeling approach cannot adequately explain three common observations in die casting foundries. First, soldering occurs much faster than the measured intermetallic growth rate based on diffusion couple experiments. Second, the alloys that are industrially observed to solder less form intermetallic phases quicker and dissolve the steel faster than alloys that solder more rapidly. Finally, the intermetallic layer is not strong enough to enable the buildup commonly observed on soldered dies. For these reasons, it is proposed that a new soldering modeling approach is required to gain a better understanding of the soldering mechanism and prevent the soldering defect in die casting.

Detection of BGA solder defects from X-ray images using deep neural network

Detection of BGA solder defects from X-ray images using deep neural network

Nondestructive characterization of solar PV cells defects by means of electroluminescence, infrared thermography, I–V curves and visual tests: Experimental study and comparison

Photovoltaic (PV) modules are the core of every PV system, representing the power generation and their operation will affect the overall plant performance. It is one of the elements within a PV site with the higher failure appearance, being essential their proper operation to produce reliable, efficient and safety energy. Quantitative analysis and characterization of manufacturing, soldering and breaking PV defects is performed by a combination of electroluminescence (EL), infrared thermography (IRT), electrical current voltage (I–V) curves and visual inspection. Equivalent-circuit model characterization and microscope inspection are also performed as additional techniques when they contribute to the defects characterization. A 60-cells polycrystalline module has been ad hoc manufactured for this research, with different defective and non-defective cells. All cells are accessible from the backside of the module and the module includes similar kinds of defects in the same bypass string. This paper characterizes different defects of PV modules to control, mitigate or eliminate their influence and being able to do a quality assessment of a whole PV module, relating the individual cells performance with the combination of defective and non-defective cells within the module strings, with the objective of determining their interaction and mismatch effects, apart from their discrete performance.

Reducing soldering defects in mobile phone manufacturing company: A DMAIC approach

A DMAIC (an effective tool) approach has been presented in this paper for reducing defects in soldering in an endeavor to approach six sigma quality level in a mobile phone manufacturing company. DMAIC helps expose and kill the root causes of defects in the process and thereby helps achieve higher customer satisfaction by means of improved quality. The case study was conducted in an electronics manufacturing industry engaged in an assembly of mobile phones. Post successful implementation of Six sigma - DMAIC in the assembly processes, ppm defectives dipped from 3800 to 200, which is a massive 94.74 percent reduction in ppm defectives.

D3PointNet: Dual-Level Defect Detection PointNet for Solder Paste Printer in Surface Mount Technology

In the field of surface mount technology (SMT), early detection of defects in production machines is crucial to prevent yield reduction. In order to detect defects in the production machine without attaching additional costly sensors, attempts have been made to classify defects in solder paste printers using defective solder paste pattern (DSPP) images automatically obtained through solder paste inspection (SPI). However, since the DSPP images are sparse, have various sizes, and are hardly collected, existing CNN-based classifiers tend to fail to generalize and over-fitted to the train set. Besides, existing studies employing only multi-label classifiers are less helpful since when two or more defects are observed in the DSPP image, the location of each defect can not be specified. To solve these problems, we propose a dual-level defect detection PointNet (D 3 PointNet), which extracts point cloud features from DSPP images and then performs the defect detection in two semantic levels: a micro-level and a macro-level. In the micro-level, a type of printer defect per point is identified through segmentation. In the macro-level, all types of printer defects appearing in a DSPP image are identified by multi-label classification. Experimental results show that the proposed D 3 PointNet is robust to the sparsity and size changes of the DSPP image, and its exact match score was 10.2% higher than that of the existing CNN-based state-of-the-art multi-label classification model in the DSPP image dataset.

Simulation and Experimental Investigation on Active Thermography Test of the Solder Balls

The miniaturized electronic products require not only high density, but also high reliability. Defect inspection for the high-density package becomes challenging gradually. In this article, active thermography technology is used to detect the solder balls in flip-chip (FC) packages. A single-layer FC model is constructed and heat conduction in the FC package is simulated by using the finite-element analysis code of COMSOL. Experimental investigation is also carried out to inspect the solder defects by using the laser excitation thermography test system. Infrared images of the SFA1 package are captured, and the spatial adaptive filtering algorithm is used to remove the thermal noise. The hot spots of the solder balls are segmented from the filtered infrared image, and the representative features are extracted for each hot spot. The modified learning vector quantization (LVQ) neural network is used for the classification of the solder balls. The missing solder balls are identified accurately. The simulation and experimental results prove that the proposed approach using active thermography and LVQ algorithm is effective for defect inspection in high-density electronic devices.

Intelligent detection of flip chip with the scanning acoustic microscopy and the general regression neural network

In the past few decades, Flip chip (FC) has been one of the mainstream technologies in IC packaging. But the size and spacing of the solder bumps are decreasing gradually, defect detection is getting more and more difficult. It is necessary to explore an efficient and accurate detection method. In this paper, the scanning acoustic microscopy (SAM) and the general regression neural network (GRNN) were investigated for inspecting the solder defect. The SAM machine of D9500 was utilized to test the samples. The solder bumps, as the region of interest, were separated from the achieved image, and transformed into the binary images. The representative features of each solder joint were extracted, and then fed to the GRNN for solder joints classification. The recognition accuracy reached 97.79%. The GRNN, combined with the SAM technology, has been validated to be an effective method for intelligent diagnosis of flip chips.

Implementation of an Automatic Optical Inspection System for Solder Quality Classification of THT Solder Joints

Machine vision has been widely deployed in many industrial applications. However, the accuracy and maturity level for the inspection of through-hole technology (THT) solder joints have yet to reach its ultimate goal. In this paper, we have presented a detailed explanation on a set of novel algorithms that can be effectively used to implement an automatic vision system that is capable of classifying the quality of THT solder joints very precisely. This vision system can be easily integrated into a soldering robotic system that performs automatic soldering on THT solder joints. The proposed vision system is capable of controlling the entire robotic system while providing automatic rework capability on the defective solder joints with much less user interaction. The experimental results indicate that the proposed system has an improved recognition rate and good resilience to noise and uneven illumination distribution.

Numerical Investigation on the Effect of Squeegee Angle during Stencil Printing Process

The high requirement of smaller size, lighter weight, and high performance Printed Circuit Board (PCB) in electronic packaging has contributed to the wide application of stencil printing for soldering process. However, during stencil printing stage contributes major concern compared to other stages in Surface Mount Technology (SMT). Unsuitable process parameters can cause the soldering defects that can lead to product failure in further processes in the production line. An investigation has been conducted to predict the real-time observation of solder paste Sn96.5Ag3.0Cu0.5 (SAC305) filling process into stencil apertures as well as print quality in stencil printing by using Computational Fluid Dynamics (CFD) approach. A 3-Dimensional stencil printing model was developed and simulated in ANSYS Fluent 17 of different angles. It is found that squeegee angle 60° to 80° has potential to obtain the good print quality of solder paste.

Defect inspection of flip chip package using SAM technology and fuzzy C-means algorithm

Solder bumps are widely used in surface mount components, which provide electrical and mechanical connection between the chip/package and the substrate. As the solder bump getting smaller in dimension and pitch, it becomes more difficult to inspect the solder defects hidden in the IC package. In this paper, an intelligent inspection method using the scanning acoustic microscopy (SAM) and the fuzzy C-means (FCM) algorithm was investigated. A flip chip package of FA10 was chosen as the test sample. The SAM tests of FA10 were carried out in C-scan mode. The sub-image of every solder bump was segmented from the SAM image. The statistical features were then calculated and adopted for clustering of solder bumps using the FCM algorithm. The recognition results of FCM reached a high accuracy of 94.3%. The intelligent system is effective for defect inspection in high density packages.

Detection of PCB Soldering Defects using Template Based Image Processing Method

In this study, a predefined template-based image processing system is proposed to automatically detect of PCB soldering defects that negatively affect circuit operation. The proposed system consists of a prototype, a camera, an image processing method and inspect process. The prototype is produced using a plastic material, depending on the focal length of the camera and the PCB size. Image processing step comprises two steps. Firstly, solder joints are determined using Fuzzy C-means clustering algorithm. Then, the center of each joint is determined. In the next step, a joint template is created that contains solder joints information. This joint template contains information about the effects of touching other joints for each joint. In this way, the inspection of soldering defects is getting shorter. Finally, each joint is only inspected for the joints specified in the template. The proposed method is evaluated on 85 real PCB image with 4250 soldering joints.

Using scanning acoustic microscopy and LM-BP algorithm for defect inspection of micro solder bumps

Micro solder bump has been widely used in electronic packaging. Currently a number of flip-chip products are developing towards miniaturization with more I/Os at finer pitch, and defect inspection of the high density package is increasingly challenging. In this paper, the Levenberg-Marquardt back-propagation network (LM-BP) combined with the scanning acoustic microscopy technology was investigated for intelligent diagnosis of solder defect. The flip chips were detected by using a 230MHz ultrasonic transducer. Solder bumps were segmented from the SAM image. The statistical features were extracted and fed into the LM-BP networks for bump classification. The results demonstrate that LM-BP algorithm reached a high recognition accuracy, and is effective for defect inspection of the micro solder bump.

Practical Application and Analysis of Lead-Free Solder on Chip-On-Flip-Chip SiP for Hearing Aids

Abstract Due to rapid growth of the microelectronics industry, packaged devices with small form factors, low costs, high power performance, and increased efficiency have become of high demand in the market. To realize the current market development trend, flip chip interconnection and System-in-Package (SiP) are some of the promising packaging solutions developed. However, a surprising amount of surface mount technology (SMT) defects are associated with the use of lead-free solder paste and methods by which the paste is applied. Two such defects are solder extrusion and tombstoning. Considerable amount of defects associated with solder overflow are found on chip-on-flip-chip (COFC) SiP in hearing aids. Through the use of design of experiments (DOE), lead-free solder defect causes on hearing aids application can be better understood and subsequently reduced or eliminated. This paper will examine the failure modes of solder extrusion and tombstoning that occurred when two different types of lead-free solders, Sn-Ag-Cu (SAC) and BiAgX were used within a SiP for attachment of surface mount devices (SMD) chip components for hearing aid applications. The practical application and analysis of lead-free solder for hearing aids will include the comprehensive failure analysis of the SMD components and compare the modeling and analysis of the two different solder types through the DOE process.

Study of pseudo soldering based on eddy current pulsed thermography

Pseudo soldering defects can break the electrical and mechanical connection between components and the print circuit board and eventually cause failure of the whole electronic equipment. In this letter, the eddy current pulsed thermography (ECPT) method was used for defect inspection of small-sized solder joints. The identification of defects of solder joints is based on the heat transfer between various component structures. The experimental results indicated that the ECPT method can be effectively used for defect detection and location of the solder joints. In addition, it can distinguish different degrees of pseudo soldering.

Localization of component lead inside a THT solder joint for solder defects classification

Purpose: Automatic Optical Inspection (AOI) systems, used in electronics industry have been primarily developed to inspect soldering defects of Surface Mount Devices (SMD) on a Printed Circuit Board (PCB). However, no commercially available AOI system exists that can be integrated to a desktop soldering robotic system, which is capable of identifying soldering defects of Through Hole Technology (THT) solder joints along with the soldering process. In our research, we have implemented an AOI platform that is capable of performing automatic quality assurance of THT solder joints in a much efficient way. In this paper, we have presented a novel approach to identify soldering defects of THT solder joints, based on the location of THT component lead top. This paper presents the methodologies that can be used to precisely identify and localize THT component lead inside a solder joint. Design/methodology/approach: We have discussed the importance of lead top localization and presented a detailed description on the methodologies that can be used to precisely segment and localize THT lead top inside the solder joint. Findings: It could be observed that the precise localization of THT lead top makes the soldering quality assurance process more accurate. A combination of template matching algorithms and colour model transformation provide the most accurate outcome in localizing the component lead top inside solder joint, according to the analysis carried out in this paper. Research limitations/implications: When the component lead top is fully covered by the soldering, the implemented methodologies will not be able to identify the actual location of it. In such a case, if the segmented and detected lead top locations are different, a decision is made based on the direction in which the solder iron tip touches the solder pad. Practical implications: The methodologies presented in this paper can be effectively used to have a precise localization of component lead top inside the solder joint. The precise identification of component lead top leads to have a very precise quality assurance capability to the implemented AOI system. Originality/value: This research proposes a novel approach to identify soldering defects of THT solder joints in a much efficient way based on the component lead top. The value of this paper is quite high, since we have taken all the possibilities that may appear on a solder joint in a practical environment.

Thermal Analysis of Solder Joint Based on Eddy Current Pulsed Thermography

Solder joint provides electronic and mechanical connections between components and substrate. Overheating and excessive temperature gradient can cause solder joint failures and then make the whole system break down. Thus, thermal analysis has been the hotspot in reliability evaluation of electronic packaging. This paper proposes eddy current pulsed thermography (ECPT) to investigate thermal transfer through solder joint in a surface mounted device (SMD). A 3-D electromagnetic-thermal model was built, and the ECPT system was established to analyze transient temperature distribution on the pin surface. This model introduced pseudo soldering and evaluated its impacts on thermal conduction. Thermal resistance of solder joint was calculated to characterize different pseudo soldering defects. With pseudo soldering acreage increased, thermal resistance had an approximate quadratic rise. Both simulation and experimental results indicate that the surface temperature gradually decreases with the increase of pseudo soldering acreage. This ECPT method could be applied to the thermal analysis of solder joint in SMD, and it also could provide guidance for thermal design or reliability evaluation of electronic packages.

基于电-热-力耦合模型功率器件焊料层失效下的热力特性分析

焊料层缺陷是引起功率器件失效的主要诱因之一，分析不同焊料层缺陷作用下功率器件的热力特性是提高功率器件可靠性的有效手段。本文首先建立了IGBT模块热-力特性分析的电-热-力多物理场全尺寸模型，该模型基于测量参数考虑了材料温度依赖性和力学特性；其次，分析了焊料层空洞和焊料层脱落分层在功率循环以及小载荷下对模块热力特性的影响规律。结果表明所建立的电-热-力多物理场模型能够较好表征焊料层空洞和焊料层脱落时功率器件热力特性的影响规律。当焊料层中出现空洞后，芯片表面温度分布发生了明显改变，高温主要集中在空洞边缘区域；当焊料层疲劳累积到一定程度后将进入非线性扩展阶段，模块热阻和结温呈指数函数增大，直至失效。 Solder defect is one of the main causes of failure in power module, and the thermal stress analysis is an effective way to improve its reliability. In the paper, an electro-thermal-mechanical coupling model is built, which considers the temperature dependent of material and mechanical characte-ristic based on measurement parameters, and the built model is verified by experimental results. In addition, the influence of solder crack on the damage accumulation, as well as the fatigue damage under power cycling and small thermal loading cases is investigated. Finally, the condition as-sessment model is proposed in considering of the effect of crack damage. Results show that the built model can well characterize the influence of the solder layer void and the solder layer shedding on the thermal characteristics of the device. When the voids in the solder appear, the temperature distribution of the chip changes obviously, and the high temperature is mainly concentrated in the hollow edge region. When the fatigue layer of the solder accumulates to a certain extent, it will enter the nonlinear expansion stage. The module thermal resistance and junction temperature exponentially increase until failure.

Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM) and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD) and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR) algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.

Comparative Modeling and Analysis of Lead-Free Solder Extrusion for the Design of Reliability

Abstract Due to rapid growth of the microelectronics industry, packaged devices with small form factors, low costs, high power performance, and increased efficiency have become of high demand in the market. To realize the current market development trend, flip chip interconnection and System-in-Package (SiP) are some of the promising packaging solutions developed. However, a surprising amount of surface mount technology (SMT) defects are associated with the use of lead-free solder paste and methods by which the paste is applied. Two such defects are solder extrusion and tombstoning. Through the use of design of experiments (DOE), lead-free solder defect causes can be better understood and subsequently reduced or eliminated. This paper will examine the failure modes of solder extrusion and tombstoning that occurred when two different types of lead-free solders, Sn-Ag-Cu (SAC) and BiAgX were used within a SiP for attachment of surface mount devices (SMD) chip components. The systematic investigation will include the comprehensive failure analysis of the SMD components and compare the modeling and analysis of the two different solder types utilizing the design of experiments methods.

Defect inspection of solder bumps using the scanning acoustic microscopy and fuzzy SVM algorithm

Flip chip technology has been extensively used in high density electronic packaging over the past decades. With the decrease of solder bumps in dimension and pitch, defect inspection of solder bumps becomes more and more challenging. In this paper, an intelligent diagnosis system using the scanning acoustic microscopy (SAM) is investigated, and the fuzzy support vector machine (F-SVM) algorithm is developed for solder bump recognition. In the F-SVM algorithm, we apply a fuzzy membership to input feature data so that the different input features can make different contributions to the learning procedure of the network. It solves the problem of feature data aliasing in the traditional SVM. The SAM image of flip chip is captured by using an ultrasonic transducer of 230MHz. Then the segmentation of solder bumps is based on the gradient matrix of the original image, and the statistical features corresponding to every solder bump are extracted and adopted to the F-SVM network for solder bump classification and recognition. The experiment results show a high accuracy of solder defect recognition, therefore, the diagnosis system using the F-SVM algorithm is effective and feasible for solder bump defect inspection.