Circuit Board Assembly Research Articles

Optimization Method for Hot Air Reflow Soldering Process Based on Robust Design

The process design of hot air reflow soldering is one of the key factors affecting the quality of PCBA (Printed Circuit Board Assembly) component products. In order to improve the product quality during the design process, this paper proposes a robust optimization-based finite element simulation analysis method including significant influencing factor screening, robustness evaluation, robust optimization, and reliability verification for the reflow soldering process. The simulation model of the reflow soldering process temperature field based on experiments is constructed and validated. Sensitivity analysis is used to select important influencing factors, such as the last five set temperature zones (T5 to T9) in the reflow oven and the thermal properties of materials such as PCBs (printed circuit boards), BGAs (ball grid arrays), and solder paste, as well as noise factors like the heating environment during the soldering process. Several surrogate models are used to construct the response surface, and the optimal fitting scheme is selected to effectively avoid poor fitting caused by inappropriate surrogate models. The 6σ robust optimization approach is introduced to evaluate and optimize the robustness of the process design parameter where the heating factor is chosen as the optimization target. The reliability analysis method is employed to validate the product quality. This paper establishes a comprehensive robustness analysis method for hot air reflow soldering, effectively reducing design costs and addressing the lack of robustness analysis in the current hot air reflow soldering process design.

Integrated Approach to Obtain Gas Flow Velocity in Convection Reflow Soldering Oven

The nozzle-matrix gas flow velocity has a great influence on the accuracy of the temperature field of a printed circuit board assembly (PCBA) during the hot air convection reflow soldering process. This paper proposes a new approach that integrates the theoretical calculation, numerical simulation and an experimental test to accurately determine the nozzle-matrix gas flow velocity. First, the temperature profile of the aluminum alloy thin plate in convection reflow ovens is measured using a Wiken tester. Second, the nozzle-matrix gas flow velocity is theoretically calculated with the Martin formula. The computational fluid dynamic (CFD)simulation is performed according to the Icepak code, where a single oven chamber model is established to represent the 10 zones of soldering ovens to reduce computational resources considering the supry of the soldering ovens. The simulated temperature profile of the aluminum alloy thin plate is obtained and the specific response surface model (RSM) is established to represent the deviation between the simulated temperature and the measured temperature. Finally, based on reverse problem analysis, non-linear programming by quadratic Lagrangian (NLPQL) is used to solve the mathematical optimization model with the objective of minimizing the temperature deviation to obtain the corrected nozzle-matrix gas flow velocity. To validate the accuracy, the temperature test and the modeling using the corrected gas flow velocity for a new PCBA component for the soldering ovens is conducted separately. The temperature comparison between the simulation and the test shows that the maximum temperature deviation is within 10 °C. This provides evidence that the nozzle-matrix gas flow velocity obtained by the new approach is accurate and effective.

Technology Modules Providing Solutions for Agile Manufacturing

In this paper, we address the most pressing challenges faced by the manufacturing sector, particularly the manufacturing of small and medium-sized enterprises (SMEs), where the transition towards high-mix low-volume production and the availability of cost-effective solutions are crucial. To overcome these challenges, this paper presents 14 innovative solutions that can be utilized to support the introduction of agile manufacturing processes in SMEs. These solutions encompass a wide range of key technologies, including reconfigurable fixtures, low-cost automation for printed circuit board (PCB) assembly, computer-vision-based control, wireless sensor networks (WSNs) simulations, predictive maintenance based on Internet of Things (IoT), virtualization for operator training, intuitive robot programming using virtual reality (VR), autonomous trajectory generation, programming by demonstration for force-based tasks, on-line task allocation in human–robot collaboration (HRC), projector-based graphical user interface (GUI) for HRC, human safety in collaborative work cells, and integration of automated ground vehicles for intralogistics. All of these solutions were designed with the purpose of increasing agility in the manufacturing sector. They are designed to enable flexible and modular manufacturing systems that are easy to integrate and use while remaining cost-effective for SMEs. As such, they have a high potential to be implemented in the manufacturing industry. They can be used as standalone modules or combined to solve a more complicated task, and contribute to enhancing the agility, efficiency, and competitiveness of manufacturing companies. With their application tested in industrially relevant environments, the proposed solutions strive to ensure practical implementation and real-world impact. While this paper presents these solutions and gives an overview of their methodologies and evaluations, it does not go into their details. It provides summaries of comprehensive and multifaceted solutions to tackle the evolving needs and demands of the manufacturing sector, empowering SMEs to thrive in a dynamic and competitive market landscape.

Knowledge-Intensive Diagnostics Using Case-Based Reasoning and Synthetic Case Generation

Due to commercial pressures, North-American printed circuit-board assembly manufacturers have had to reposition themselves in the more difficult market segment of lower volume, higher complexity products, also called High-Mix, Low-Volume (HMLV). The high per-unit costs of these products justify substantial diagnosis and repair efforts when defects are detected in production. Although automated diagnostics is desirable, the low production volumes impose severe limits on available data. We propose a novel approach based on knowledge modeling and casebased reasoning for automated diagnosis of assembled printed circuit boards in an HMLV production environment. Our hybrid approach can overcome the knowledge-acquisition bottleneck even in a data-poor environment. The proposed approach does not require contributions from product designers or experts and is designed to operate using only information available during manufacturing. Our test results show that our diagnostic system can detect, locate and classify all single faults and multiple faults affecting up to three neighboring nodes with a better success rate than the reference commercial tool. Moreover, case base data, including board layout information and user feedback from previous repairs, is used to feed a recommender system to provide repair suggestions. A production and repair data simulator is described and used to evaluate the recommender system’s effectiveness.

Discrete Event Simulation modelling as an improvement validation tool on Printed Circuit Board Mechanical Assembly process

Although the application of Discrete Event Simulation (DES) as a validating tool may seem counterproductive, routinely reviewing an improvement that has been installed is crucial for industries to decide whether a process is optimal, requires further refinement, or must be discontinued and replaced with a better alternative. The objective of this paper is to demonstrate DES as a decision-making tool by validating the improvements of a mechanical assembly line for Printed Circuit Board Assembly (PCBA) manufacturing. As a case study, parameters connected to Overall Equipment Effectiveness (OEE) namely availability, performance, and quality were gathered from a PCBA mechanical assembly line in Company A that has shifted their primary assembly tools from conventional electrical torque screwdrivers into programmable torque screwdrivers. Through the data collection step, it was observed that the OEE improved upon implementing a programmable torque screwdriver. The assembly lines were then modelled in the DES system and information pertaining to OEE was gathered and analyzed to aid in validating the enhancement in the manufacturing line. Based on the simulation, the results obtained confirm that the OEE improves through the implementation of a programmable torque screwdriver, signifying that DES is a suitable tool to help validate an improvement in a process.

Performance analysis of avionics devices based on electro-thermal-stress multi-physics coupling under immersion cooling

The present study aims to analyze immersion cooling for the work reliability enhancement of avionics devices based on electro-thermal-stress multi-physics simulation. A comprehensive performance indicator considering the maximum DC IR-drop of the power supply network (PDN), the maximum temperature of the electronic elements and the maximum deformation of the printed circuit board (PCB) assembly is constructed to evaluate the effect of different flow distributor structure styles and Reynolds number (Re) on the work reliability of avionics devices. The results show that the maximum DC IR-drop of 2.0 V PDN was 73.83 mV which was the largest value among 1.5 V, 2.0 V, 3.3 V and 5.0 V PDN. The DC IR-drop of design A, design B and design C was smaller than the supply voltage fluctuation in a real design, this indicates that the flow distributor has no significant effect on enhancing the power delivery capability of PDN. The reflow in the middle part of design C makes the average temperature of PCB assembly lower than that of design A and design B by about 1–3 °C. The flow distributor has a significant effect on the maximum temperature of electronic elements and the pressure drop of avionics devices with Re increase, but not viable to decrease the maximum deformation of PCB assembly for design A and design B. The minimum of comprehensive evaluation function f is 0.038 when Re = 43,000 for design C, this means the comprehensive working reliability of the avionics server module is the best. Due to different flow patterns, the average temperature of PCB assembly for design C was lower which improve the electrical and mechanical properties, thus the value of f for design C was always smaller than that of design A and design B. The method and results herein revealed the advantages and potential limitations of immersion cooling on the working reliability of the avionics devices.

An efficient SMD-PCBA detection based on YOLOv7 network model

Modern Printed Circuit Board Assembly (PCBA) manufacturing processes require more accurate and robust defect inspection methods. Despite the potential of deep learning algorithms in PCBA defect detection, their ability to handle environmental factors and multi-modal data is still limited. To overcome this, we propose an improved YOLOv7-based network model that enhances the detection performance of densely distributed multi-size Surface Mount Devices (SMD) in multi-modal PCBA. Specifically, the proposed model enhances feature representation by designing a detection head based on Coordinate Attention, and incorporates feedback connections in the feature fusion stage to improve feature recognition through low-level propagation. Additionally, we propose the SEIoU loss function to calculate position loss between the prediction box and the ground truth, resulting in superior regression accuracy of the anchor box and improved detection accuracy. We validate the effectiveness and improvement of our proposed method through ablation experiments and algorithm comparison. Our proposed model outperforms the baseline YOLOv7 model with a 2.1% increase in mAP@0.5 for the multi-modal PCBA dataset and a 4.5% increase in mAP@0.5:0.95 for the VOC 2012 dataset, and a 1.0% increase in mAP@0.5:0.95 for the COCO 2017 dataset. Our study’s results suggest that our proposed model is a promising alternative to existing methods for detecting PCBA defects, as it accurately detects multiple tiny components amidst complex backgrounds, effectively identifies diverse types of defects, and remains lightweight.

Miniaturized Dual Antiphase Patch Antenna Radiating into the Human Body at 2.4 GHz.

An on-body antenna, comprised of two closely-spaced antiphase patch elements, for microwave imaging may provide enhanced signal penetration into the tissue. By further integrating a 180-degree on-chip power combiner with the dual antiphase patch antenna element, a low-profile miniaturized antenna, integrated into a single 18.5 mm x 10 mm x 1.6 mm circuit board assembly, is designed and evaluated both numerically and experimentally. This is the smallest on-body antenna known to the authors for the given frequency band. This linearly polarized antenna may potentially serve as a building block of a dense antenna array for prospective high-resolution microwave imaging. A 2.4 GHz band was chosen as the design target. The final antenna size was a compromise between the miniaturization, the SNR (Signal-to-Noise Ratio), and the targeted antenna bandwidth (2.3-2.5 GHz). The effect of surface waves (the secondary radiating components) was also factored in the design consideration, while maximizing the detected signals' SNR.

Intuitionistic fuzzy fault tree analysis of PCBA using novel arithmetic operations

PurposeThe main goal of this paper is to develop novel Tω(weakest t-norm)-based fuzzy arithmetic operations to analyze the intuitionistic fuzzy reliability of Printed Circuit Board Assembly (PCBA) using fault tree.Design/methodology/approachThe paper proposes a fuzzy fault tree analysis (FFTA) method for evaluating the intuitionistic fuzzy reliability of any nonrepairable system with uncertain information about failures of system components. This method uses a fault tree for modeling the failure phenomenon of the system, triangular intuitionistic fuzzy numbers (TIFNs) to determine data uncertainty, while novel arithmetic operations are adopted to determine the intuitionistic fuzzy reliability of a system under consideration. The proposed arithmetic operations employ Tω(weakest t-norm) to minimize the accumulating phenomenon of fuzziness, whereas the weighted arithmetic mean is employed to determine the membership as well as nonmembership degrees of the intuitionistic fuzzy failure possibility of the nonrepairable system. The usefulness of the proposed method has been illustrated by inspecting the intuitionistic fuzzy failure possibility of the PCBA and comparing the results with five other existing FFTA methods.FindingsThe results show that the proposed FFTA method effectively reduces the accumulating phenomenon of fuzziness and provides optimized degrees of membership and nonmembership for computed intuitionistic fuzzy reliability of a nonrepairable system.Originality/valueThe paper introduces Tω(weakest t-norm) and weighted arithmetic mean based operations for evaluating the intuitionistic fuzzy failure possibility of any nonrepairable system in an uncertain environment using a fault tree.

Surrogate Modeling for Creep Strain-Based Fatigue Prediction of a Ball Grid Array Component

AbstractIn the past years, the finite element analysis (FEA) has proven to be a suitable way for fatigue prediction of electronic equipment based on the physics-of-failure-approach. For this, inelastic strain parameters like creep strain or creep energy density are evaluated in crack susceptible regions of solder joints. Due to the nonlinearity of the creep behavior, which is the basis for these simulations, the computational effort can be significant. This mostly leads to a component-focused approach. Global influences on components like local stiffness variations due to adjacent components, copper traces, or fixations of the printed circuit board (PCB) are often ignored. To make creep-based fatigue predictions suitable for complex printed circuit board assemblies (PCBA), a method for reducing computational effort needs to be established. For this matter, a machine learning-based approach for solder joints has been developed. First, the process for data generation and model training has been established. Thereafter, several methods for input parameter reduction are discussed. Finally, a model is being trained based on the generated simulation data.

Optimization of Additively Manufactured Interposers for DC and RF Applications in Printed Circuit Boards

Additive manufacturing (AM) for printed electronics (PE), though still in its infancy, has seen major growth in the past few years. AM techniques have several benefits over traditional manufacturing, namely the reduction of waste material and the increased efficiency in producing prototypes. However, there are disadvantages associated with AM, the most prevalent being a low throughput of parts. Limited research has been done to increase the throughput to the scale of traditional manufacturing methods. To combine the benefits of both AM and traditional manufacturing techniques, this paper discusses a hybrid printed circuit board (PCB) assembly that employs an additively manufactured interposer to serve as a DC interconnect between the AM structure and the PCB manufactured with traditional methods.

Mechanical Characterization of SAC305 and SnPb36Ag2 BGA Assemblies Under Static Flexural Loading

Static bending-induced solder joints damage is a main reliability concern for aerospace and military industries whose Printed Circuit Board Assemblies (PCBAs) are required to remain functional under flexural loading. In order to dissipate heat in an equipment, it is common to install thermal gap pads on electronic packages. When compressing thermal gap pads during the fixture process, the PCBA can bend and solder joints can therefore crack if the deflection is too important. This paper reports the durability of 96.5Sn-3.0Ag-0.5Cu (SAC305) and 62Sn-36Pb-2Ag (SnPb36Ag2) Ball Grid Array (BGA) assemblies under static flexural loading at -55°C, 20°C and 125°C. As electronic equipment can be stored at high temperature for prolonged durations, some SAC305 test vehicles were also aged at 125°C for 192 hours. For each test configuration, the bending tests were conducted at a ramp-rate of 2 mm/min and the central displacement-to-failure was measured. Finite Element Modeling (FEM) analysis was conducted considering a global-local approach and the transfer function between the central displacement-to-failure and the local PCB strain near the critical solder joints was determined for each test configuration. The results give the necessary data for designers to assess whether a specific PCBA design subjected to static bending is at risk or not.

A Variable Configuration Force Sensor With Adjustable Resolution for Robotic Applications

Multitask measurements with different measuring ranges and resolutions have put forward higher requirements for force sensors. This article presents a novel variable configuration approach to develop an adjustable-resolution force sensor with a compact force-sensitive structure. Unlike the traditional strain gauge-based force measurement, the external force is detected through measuring the displacement of the deformed structure with a precision optical linear encoder. This approach can enable variable measuring range and force resolution through the continuous adjustment of structural stiffness. Specifically, the stiffness adjustment is implemented by changing the second moment of area of the sensor structure through rotating its build-in flexure beam shafts. Analytical models are established to provide the theoretical basis for analysis and design of the sensor. Furthermore, the validity of the design is evaluated with finite element simulations. Finally, a sensor prototype is developed for performance evaluation, and applied for the wire bond pull tests to evaluate the bond strength on the printed circuit board assembly. Experimental results show that it is effective to change the measuring range and force resolution of the developed force sensor through the stiffness adjustment.

Direct Writing of Liquid Metal onto an Electrospun Graphene Oxide Composite Polymer Nanofiber Membrane for Robust and Stretchable Electrodes

AbstractHighly stretchable electrodes with electrically robust behavior are critical for wide applications of soft robots, electronic skins, and flexible sensors. However, it remains challenging to fabricate such electrodes with traditional fabrication methods, such as lithography, conductive composite material synthetization, stencil printing, and microchannel injection. Herein, a facile method is proposed to construct robust and stretchable electrodes by direct‐written liquid metal (LM) onto a predeposited interface bonding layer, which greatly improves the interfacial force between the LM and substrate. An electrospun graphene oxide/thermoplastic polyurethane composite nanofiber membrane is used as the bonding layer, which provides rich –OH on the interface and in situ forming of hydrogen bond (H‐bond) with the LM oxide layer. A prototype electrode shows stretchability of 580%. The resistance remains stable that varies from 2.8 to 19.3 Ω at 500% elongation, and varies slightly after 7500 stretching cycles under 50% elongation, from 2.6–4.0 to 4.4–6.4 Ω. The fabrication technique is demonstrated with applications in stretchable circuit board assemblies and stretchable electronic cables, indicating a potential effective method for fabricating high‐performance stretchable electrodes.

Research on a new three proofing technique for the electric meter industry

The operational environment of industrial electronic products is more rigorous than that of household electronic products. Three Proofing (moisture proofing, salt spray proofing, and fungus proofing) has become an important topic in the electronics industry. As the core component of electric equipment, the PCBA (Printed Circuit Board Assemblies) needs extra protection. PCBA surface coating is widely used in the electric meter industry. The voltage sampling circuit and the current sampling circuit are the core parts of the electric meter, which are more sensitive to the influence of moisture, salt spray, and fungus. The traditional surface coating method comes into effect on Three Proofing to some extent, but it also takes some unfavorable factors. A new Three Proofing technique is proposed via the special treatment of solder paste. Anti crack resin and other components are added to the solder paste in a certain proportion. During Reflow Soldering Process, the resin melts and adsorbs on the surface of the solder joint, which forms a protective layer for the solder joint. Reliable experimental verification shows that the proposed technology not only has the function of the Three Proofing, but also solves the unfavorable factors of the traditional process. This provides a new feasible solution for Three Proofing of the electric meter industry.

Effects of Concentration of Adipic Acid on the Electrochemical Migration of Tin for Printed Circuit Board Assembly

Effects of Concentration of Adipic Acid on the Electrochemical Migration of Tin for Printed Circuit Board Assembly

Selection of Semiconductor Packaging Materials by Combined Fuzzy AHP-Entropy and Proximity Index Value Method

The residue of no-clean flux (NCF) on the printed circuit board assembly has been one of the major reasons for corrosion reliability failure of electronic devices. The source of aggressive contamination is the carboxylic acid activator as the main cleaning agent for soldering surfaces. Selecting the suitable activator has always been one of the concerns due to its conflicting properties, for example, suberic acid has a lower boiling point that will help in minimizing the residue; however, its dissociation constant is low which will increase the aggressive residue. The selection of a suitable activator based on the conflicting criteria is a multicriteria decision-making (MCDM) problem. In this study, thirty-one carboxylic acids (mono, di, tri, and hydroxyl) were ranked based on the six different criteria, i.e., melting point, molecular weight, boiling point, acid dissociation constant (pKaa value), solubility, and number of carboxylic acids using combined fuzzy AHP-entropy and the proximity index value method. Weight of the criterion was calculated using combined fuzzy AHP-entropy; whereas, ranking of the alternative carboxylic acid was done by the proximity index value method. First, the ranking was done based on the overall carboxylic acid; consequently, the groupwise ranking was also evaluated. As a result of this study, margaric acid occupied rank 1 out of all the thirty-one carboxylic acids and monocarboxylic acids; whereas, malonic acid acquired the lowest rank. In addition, out of 9 dicarboxylic acids, sebacic acid was the most suitable alternative. In the case of tricarboxylic acids, trimesic acid was a suitable alternative. Sensitivity analysis was performed to verify the reliability and consistency of the result obtained through applied MCDM.

Federated-Learning-based Decision Support for Industrial Internet of Things (IIoT)-based Printed Circuit Board Assembly Process

Federated-Learning-based Decision Support for Industrial Internet of Things (IIoT)-based Printed Circuit Board Assembly Process

Solder Joint Defect Inspection Method Based on ConvNeXt-YOLOX

ConvNeXt-YOLOX model, an improved deep learning algorithm based on the YOLOX neural network, is proposed for surface mount technology (SMT) production line printed circuit board assembly (PCBA) solder joint defect detection with high accuracy and speed. The improved model employs the classification neural network ConvNeXt to enhance the feature extraction capability of the YOLOX backbone feature extraction network, thus enhancing the target detection accuracy and speed as well as the small target detection capability while maintaining the same number of parameters. A solder joint defect detection dataset containing 759 PCBA solder joint defect images is constructed, on which the improved model ConvNeXt-YOLOX, the original model YOLOX, and the lightweight model YOLOX-s are trained. Subsequently, the training results of the three models are compared. The comparison shows that the mean average precision (mAP) of the improved model ConvNeXt-YOLOX is 97.21%, which is 0.82% and 3.02% higher than that of YOLOX and YOLOX-s, respectively, while the mAP at (0.5:0.95) is increased from 76.3 to 77.5. Moreover, the detection speed is increased from the original 27.06 to 27.88 frames/s. In summary, the improved model ConvNeXt-YOLOX has strong small target feature extraction and detection capabilities, which are consistent with the actual requirements of solder joint defect detection.

THERMAL-INDUCED DAMAGE ON SOLDER JOINTS OF HIGH-DENSITY ADJACENT BALL GRID ARRAY COMPONENTS DURING THE REWORK PROCESS

Correlation between adjacent ball grid array (BGA) components' temperature and thermal-induced damage to their solder joints on both the top and bottom printed circuit board assembly (PCBA) sides during the rework process is studied in this work. Total often adjacent BGA components from the rework area on the top (F - K) and bottom (A - E) PCBA side were observed. The dye and pull (D&P) test results, infrared thermography images, and temperature measurements were analyzed for solder joint crack induced by the thermal damage with precise temperature measurement on each adjacent BGA component. Scanning electron microscope (SEM) analysis complemented the post-D&P test solder joint crack observation. The D&P test results shows that 76 % or higher and solder joint separation locations at the copper pad on BGA occurred when the temperature of the adjacent components of the rework location exceeded 195 °C and 210 °C on the centre and corner of the component, respectively.