Printed Circuit Board Components Research Articles

Investigation of home appliances industry and devices obsolescence considering energy consumption

The objective of this study is to optimize a linear objective function that pertains to the obsolescence of home appliances in a household. The approach introduced is an ILP-based method that is both simple and adaptable. This optimization guarantees that the total energy consumption remains below a predetermined energy threshold, while also adhering to an inequality constraint. The proposed methodology was effectively tested using a household containing four home appliances: a washing machine, a dishwasher, a refrigerator, and an oven. The findings indicate that the refrigerator is the most hazardous and inexpensive home appliance. The current formulation aims to optimize the device's lifespan considering the customer's financial constraints and the expenses associated with repairing and replacing its parts. This study centers on the maintenance of printed circuit boards (PCBs) and considers the effects of degradation-related malfunctions, as assessed through testing. Moreover, we employ an additional ILP approach to assess the vulnerability of refrigerator’s PCB components by determining which ones should be replaced when a customer has limited financial resources.

EC-YOLO: Improved YOLOv7 Model for PCB Electronic Component Detection.

Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model's size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition.

Research on PCB layout optimization of vehicle controller based on electrothermal hybrid simulation

Abstract With the rapid development of the electronic industry, electronic equipment is more and more widely used in automobiles. The integration of electronic components on the Printed Circuit board (PCB) is getting higher and higher, the size is getting smaller and smaller, and the power is getting larger and larger, which leads to a sharp increase in the heat flow density of the whole PCB. The surface temperature of the chip is too high when it is working, which leads to a series of thermal failure problems. In order to improve the thermal characteristics between PCB and electronic components and improve the heat dissipation capability and reliability of PCB, this paper designed a vehicle controller PCB. PowerDC was used to conduct an electrothermal hybrid simulation analysis, and the temperature distribution of the whole board was obtained. Meanwhile, the structural layout of the PCB was optimized by increasing the heat dissipation hole, floor copper foil, and wiring width. Thus, the temperature of PCB and electronic components is reduced, which provides a vital reference for the thermal design of PCB.

Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Microelectronics and electronic products are integral to our increasingly connected world, facing constant challenges in terms of quality, security, and provenance. As technology advances and becomes more complex, the demand for automated solutions to verify the quality and origin of components assembled on printed circuit boards (PCBs) is skyrocketing. This paper proposes an innovative approach to detecting and classifying microelectronic components with impressive accuracy and reliability, paving the way for a more efficient and safer electronics industry. Our approach introduces significant advancements by integrating optical and X-ray imaging, overcoming the limitations of traditional methods that rely on a single imaging modality. This method uses a novel data fusion technique that enhances feature visibility and detectability across various component types, crucial for densely packed PCBs. By leveraging the WaferCaps capsule network, our system improves spatial hierarchy and dynamic routing capabilities, leading to robust and accurate classifications. We employ decision-level fusion across multiple classifiers trained on different representations—optical, X-ray, and fused images—enhancing accuracy by synergistically combining their predictive strengths. This comprehensive method directly addresses challenges surrounding concurrency, reliability, availability, and resolution in component identification. Through extensive experiments, we demonstrate that our approach not only significantly improves classification metrics but also enhances the learning and identification processes of PCB components, achieving a remarkable total accuracy of 95.2%. Our findings offer a substantial contribution to the ongoing development of reliable and accurate automatic inspection solutions in the electronics manufacturing sector.

Micro-Satellite Systems Design, Integration, and Flight.

Within the past decade, the aerospace engineering industry has evolved beyond the constraints of using single, large, custom satellites. Due to the increased reliability and robustness of commercial, off-the-shelf printed circuit board components, missions have instead transitioned towards deploying swarms of smaller satellites. Such an approach significantly decreases the mission cost by reducing custom engineering and deployment expenses. Nanosatellites can be quickly developed with a more modular design at lower risk. The Alpha mission at the Cornell University Space Systems Studio is fabricated in this manner. However, for the purpose of development, the initial proof of concept included a two-satellite system. The manuscript will discuss system engineering approaches used to model and mature the design of the pilot satellite. The two systems that will be primarily focused on are the attitude control system of the carrier nanosatellite and the radio frequency communications on the excreted femto-satellites. Milestones achieved include ChipSat to ChipSat communication, ChipSat to ground station communication, packet creation, error correction, appending a preamble, and filtering the signal. Other achievements include controller traceability/verification and validation, software rigidity tests, hardware endurance testing, Kane damper, and inertial measurement unit tuning. These developments matured the technological readiness level (TRL) of systems in preparation for satellite deployment.

Exploring microbial adaptation of immobilised acidophilic cultures to improve microbial oxidation rates and copper tolerance in e-waste bioleaching

The rate of ferric iron regeneration by microbial oxidation is the rate determining step in the bioleaching of printed circuit boards (PCBs). Therefore, improvement of the volumetric rate of ferric iron formation is key for the overall bioleaching efficiency. The volumetric rate is impacted by both the specific rate and the biomass concentration. Limiting the exposure of the bioleaching microorganisms to accumulated toxic metal ions in solution, as well as the presence of inhibiting material components in PCBs through biofilm formation and immobilisation, as well as their adaptation to these inhibitors, is proposed to mitigate against decrease in specific rate while biomass retention by immobilisation is expected to positively impact volumetric rate. In this study, a mixed mesophilic culture, consisting of Leptospirillum (L.) ferriphilum, Acidithiobacillus (At.) caldus, Acidiplasma (Ap.) cupricumulans as dominant species was immobilised on polyurethane foam (PUF) and adapted to 6.0 g/L Cu2+. The rate of microbial ferrous iron oxidation by the immobilised culture was compared to that of planktonic cultures. Further, the performance of both the planktonic and immobilised Cu-adapted cultures (6.0 g/L of Cu2+) were compared with non-adapted planktonic and immobilised cells in the presence of increasing Cu2+ concentrations (0–50 g/L Cu2+).Both the Cu-adapted planktonic cells and the non-adapted immobilised cells demonstrated improved tolerance to Cu2+ stress relative to non-adapted planktonic cells. The Cu-adapted planktonic cells showed higher volumetric ferrous iron oxidation rates (0.0359 ± 0.0023 g Fe2+.L−1.h−1 at 22 g/L Cu2+) than the non-adapted immobilised cells (0.0331 ± 0.0021 g Fe2+.L−1.h−1) in the presence of Cu2+ concentrations exceeding 6 g/L. The Cu-adapted immobilised cells exhibited higher tolerance to Cu2+ stress and higher oxidation kinetics (0.0622 ± 0.0064 g Fe2+.L−1.h−1) than the Cu-adapted planktonic and non-adapted immobilised cells. This study provides a novel approach for improving culture performance and minimising the inhibitory effect of Cu2+ ion on L. ferriphilum dominated cultures. High microbial activity and metal tolerance of Cu-adapted immobilised cells presents an opportunity for immobilised microbial systems to be used both in bioleaching of e-waste streams, such as PCBs, and in bioleaching of mineral concentrates, ores and wastes with Cu2+-rich leachates.

Defect Detection Model of Printed Circuit Board Components Based on the Fusion of Multi-Scale Features and Efficient Channel Attention Mechanism

Defect Detection Model of Printed Circuit Board Components Based on the Fusion of Multi-Scale Features and Efficient Channel Attention Mechanism

Synthetic Data Generation for Text Spotting on Printed Circuit Board Component Images

Synthetic Data Generation for Text Spotting on Printed Circuit Board Component Images

Detection of Missing Component in PCB Using YOLO

The detection of missing components in printed circuit boards (PCBs) is a critical task in the electronics manufacturing industry. The current practice of manual inspection is time-consuming and prone to human error, which can result in faulty products and increased costs. In this paper, we propose a solution that uses the YOLO (You Only Look Once) object detection algorithm to automatically detect missing electronic components in PCBs. Electronic components detection model is trained using YOLOv3 architecture. Dataset is prepared using high quality printed circuit board images and manual labeling in Label Studio. The model is trained on a dataset of 16 different electronic components commonly found in PCBs including Electrolytic Capacitor, QFP, Toroidal core Inductor, Crystal Oscillator etc. Prepared model recognizes these electronic components with an average map score of 65.8%with IoU 50% and 42.6% with IoU 95%. The results show that the proposed solution can detect the missing components.

Characterization of a printed-circuit board by X-ray fluorescence and X-ray diffraction analyses for metal recovery

A printed-circuit board (PCB) powder sample obtained by cryo-milling was characterized to elucidate its elemental composition and the chemical states of its components for metal recovery. The major elements (Al, Si, Ca, Fe, Ni, Cu, Sn, Ba, and Pb) were characterized by X-ray fluorescence and X-ray diffraction analyses. The particle-size distribution histogram of the ground PCB exhibited two high frequencies corresponding to the 27–45 μm and 250–500 μm fractions, and the median size was 125 μm. The elemental components of the PCB particles were categorized into four groups: (1) the elements concentrated in the coarse-particle fraction (Cu), (2) elements tending to accumulate in the moderately sized-particle fraction (Br and Sn), (3) elements concentrated in the fine-particle fraction (Fe), and (4) elements that prefer to concentrate in both the coarse- and fine-particle fractions or no particular fraction (Al, Si, Ca, Ni, Ba, and Pb). Based on the diffraction patterns of each fraction, Al existed as metallic Al and Al2O3 (corundum), Si as metallic Si, Ca as CaCO3 (calcite), Fe as Fe3O4 (magnetite), Ni as metallic Ni, Cu as metallic Cu, Ba as BaTiO3 (barium titanate), and Pb as PbZrO3 (lead zirconate). Moreover, multivariate statistical analyses (principal component and cluster analyses) revealed that the amorphous components comprised mainly Si, Ca, Br, and Sn. The elemental composition and crystalline-phase analyses revealed the following: (1) the metallic components were durable because of their high ductility and malleability; (2) the ceramic materials were concentrated in the fine-particle fraction owing to their fragile nature; (3) the PCB components exhibited different durability properties depending on their chemical states.

SolderNet: Towards trustworthy visual inspection of solder joints in electronics manufacturing using explainable artificial intelligence

AbstractIn electronics manufacturing, solder joint defects are a common problem affecting a variety of printed circuit board components. To identify and correct solder joint defects, the solder joints on a circuit board are typically inspected manually by trained human inspectors, which is a very time‐consuming and error‐prone process. To improve both inspection efficiency and accuracy, in this work, we describe an explainable deep learning‐based visual quality inspection system tailored for visual inspection of solder joints in electronics manufacturing environments. At the core of this system is an explainable solder joint defect identification system called SolderNet that we design and implement with trust and transparency in mind. While several challenges remain before the full system can be developed and deployed, this study presents important progress towards trustworthy visual inspection of solder joints in electronics manufacturing.

SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence

In electronics manufacturing, solder joint defects are a common problem affecting a variety of printed circuit board components. To identify and correct solder joint defects, the solder joints on a circuit board are typically inspected manually by trained human inspectors, which is a very time-consuming and error-prone process. To improve both inspection efficiency and accuracy, in this work we describe an explainable deep learning-based visual quality inspection system tailored for visual inspection of solder joints in electronics manufacturing environments. At the core of this system is an explainable solder joint defect identification system called SolderNet which we design and implement with trust and transparency in mind. While several challenges remain before the full system can be developed and deployed, this study presents important progress towards trustworthy visual inspection of solder joints in electronics manufacturing.

Study on the Influence of Vacuum Vapor Phase Soldering Process Technology on SiP Component

Vapor phase soldering (VPS) is used in the assembly of highly reliable electronic products for aerospace, military, and industrial area for the advantages of heat transfer coefficient, good temperature uniformity, and controllable maximum soldering temperature. VPS is usually used for PCB (Printed Circuit Board) and complex components. However, little research has been done on the application of VPS process technology on the SiP (System in Package) component in the process of solder ball or solder column package. In this work, we investigated the influence of the vacuum VPS process on the SiP component. The advantages of VPS and vacuum process were discussed and the maximum shear strength of solder joints in the vacuum process was calculated. The vacuum process has no impact on the internal structure of the SiP component.

Research Progress of Electroplated Micropore for Interconnection Technology

Electroplated micropore interconnection is one of the core technologies to realize the electronic connections of the high-density multilayer Printed circuit board (PCB). The quality of electroplated copper layer is a key factor for the reliability of PCB and electronic components. Focusing on the acid copper plating process, this paper expounds the development of the electroplating solution and additives, the mechanisms of electroplated copper deposition and hole filling mechanism, and the process control of electroplating, which will provide a reference for the in-depth study of the electroplating micropore interconnection technology.

On-line Identification of Delay Attacks in Networked Servo Control

The paper discusses attacks on networked control loops by increased delay, and shows how existing round trip jitter may disguise such attacks. The attackers objective need not be de-stabilization, the paper argues that making settling time requirements fail can be sufficient. To defend against such attacks, the paper proposes the use of joint recursive prediction error identification of the round trip delay and the networked closed loop dynamics. The proposed identification algorithm allows general defense, since it is designed for delayed nonlinear dynamics in state space form. Simulations show that the method is able to detect a delay attack on a printed circuit board component mounting servo loop, long before the attack reaches full effect.

A new approach to designing easily recyclable printed circuit boards

Due to the ever-increasing amount of electronic waste (e-waste) worldwide, the problem of the effective disposal of printed circuit board waste (WPCB), which are environmentally hazardous, difficult to recycle and economically valuable products, has become a major environmental challenge. Conventional WPCB recycling techniques have low efficiency and require tough processing, such as heat treatment and high pressure. This paper presents a new composite material for the manufacture of printed circuit boards (PCB) that can be easily recycled into their original components and reused. In addition, the most valuable PCB components (electronic components containing precious metals) can be easily separated from the printed circuit board and reused. This study demonstrates the benefit of using biodegradable polymers as binders for PCBs in terms of environmentally friendly and efficient recycling.

Exfoliation of coarse printed circuit boards using dimethylacetamide: Production of copper concentrates

Conventional hydrometallurgy-based e-waste value recovery technique employs milled printed circuit boards (PCBs) to facilitate the reaction between the metal fraction and leaching reagents. The current work investigates value recovery using coarse PCBs (1 cm × 1 cm). Thus, organic swelling pre-treatment using dimethylacetamide is employed to produce swollen and delaminated PCBs. The adopted experimental conditions were 160 °C, solid: liquid ratio of 1:10, 150 rpm and organic swelling time (15-75 min). The field emission scanning electron microscope (FESEM) images explicitly illustrated the noticeable cracks propagation over time at different sites within the multi-layered structure of PCBs. This resulted in various delaminated PCB components (i.e. halved PCBs, compound unit, copper-glass fibre cloth laminate, copper foil and glass fibre cloth), though the delamination extent varies with time. Copper (Cu) content analysis performed on the delaminated PCB components indicated that a copper concentrate with high Cu contents could be obtained (inner Cu foil: 83.39 %; outer Cu: 88.57 %) by performing organic swelling for coarse PCBs. The swollen particles can be subjected to a less energy-intensive mechanical method to yield embedded copper foils, and it is feasible due to the pre-weakened PCBs after undergoing organic swelling. Since the removal of the electronic components is a prerequisite in organic swelling, hindrances that occurred during delamination were also identified in this work. The presence of the through-hole pins in PCBs in one such condition potentially slows down the organic swelling. The results of this work could be employed to develop alternative PCBs-based value recovery flowsheets to extract the encapsulated inner copper avoiding comminution, which is energy intensive.

A Parylene-Based Ultra-Thin Printed Circuit Board As a New Platform for Flexible Sensors and Wearables

Flexible electronics and sensors are a key enabling element for the realization of wearables and geometry adaptive devices needed to follow current trends such as the Internet of things or Industry 4.0. Within this paper, we present a new and flexible packaging platform by the fabrication of an ultra-thin and highly flexible printed circuit board (PCB).The thermoplastic polymer Parylene, which combines a variety of unique properties such as optical transparency, biostability and biocompatibility according to ISO 10993, thermal stability as well as low permeability for gases and water, was used as the base for the new PCB.Particularly, its mechanical properties, especially the absence of intrinsic stresses, as well as its low Young’s modulus and good bendability are advantageous when using Parylene films as a free-standing substrate for flexible applications. The chemical inertness of Parylene against all common acids, bases and solvents ensures its compatibility with established standard microsystem technologies.For the realization of ultra-thin flexible PCBs, Parylene was used as a substrate as well as for the encapsulation and protection layer, respectively. Furthermore, Parylene was the dielectric between the metallic conductive layers. These redistribution layers (RDL) were deposited and patterned by standard microsystem technologies, such as sputtering, lithography and wet chemical etching. Hence, structure sizes of down to 10 µm were successfully realized. Different metals, such as gold, copper, and aluminum were tested for the RDL. Alternatively, printing technologies such as screen printing and aerosol jet printing were successfully demonstrated for the if conductive silver layerse. The electrical connection between the various RDL was of special interest throughout the study and was realized by etching vertical vias through the Parylene using oxygen plasma. Due to the low total thickness of the flexible Parylene PCB of 20 µm or even less, the obtained vias through the Parylene feature an advantageous low aspect ratio. For the contact formation through the vias, different methods were investigated, e. g. sputtering, printing and electrochemical deposition.Finally, the fabricated flexible Parylene PCB were characterized with respect to their bendability and electrical properties. Doing so, the resistivity and capacitance were measured as well as the frequency response between 20 kHz and 500 MHz. Particularly, for frequencies below 1 MHz the realized flexible Parylene PCBs show a good electrical performance.A third function of Parylene with respect to the realization of an ultra-thin and flexible PCB is its usage as a barrier or capping layer. Doing so, new approaches such as embedding electronic or sensor components in the flexible PCB can be enabled. Other integration technologies such as wire bonding, printing or gluing were investigated with respect to their usability for the new flexible PCB as well. Besides, also the direct fabrication of sensors on the Parylene flexible PCB was successfully demonstrated by the realization of a flexible potentiometric pH sensor.The presented new type of ultra-thin PCB is a versatile platform for the realization of flexible electronics, sensors and devices. Due to its biocompatible properties, the flexible Parylene PCB can be used for medical applications but also for the integration of MEMS, chemical sensors and optical components, respectively. Doing so, it enables the realization of smart systems for wearable applications and their integration into light weight constructions. Within this paper, the concept of this new approach, the respected fabrication and integration technologies as well as possible applications are presented in detail. Figure 1

Prediction of Production Line Status for Printed Circuit Boards

This paper focuses on the problem of predicting production line status for Printed Circuit Boards (PCBs). The problem contains three prediction tasks regarding PCB producing process. Firstly, data exploration is carried out and it reveals several data challenges, including data imbalance, data noise, small sample size, and component difference. To predict production line status for components of PCBs using records of inspection on pins, we proposed two possible feature extraction methods to compress the pin-level data into component-level. A statistical feature extraction method, which retrieves descriptive statistics such as mean, standard deviation, maximum, and minimum of pins on the same component, is applied to Task 1, while a PinNumber-based feature extraction method, which keep original values for 2-pin components, is applied to Task3. In addition, a neural-net model with feeding imbalance control is established for Task 1. and a random forests model is applied for both Task 2 and Task 3. Moreover, a threshold moving technique is proposed to optimize the threshold selection. Finally, the result shows that our models achieved f1-scores of 0.44, 0.54, and 0.71 on the test set for the three tasks, respectively.

Prediction of Production Line Status for Printed Circuit Boards

This paper focuses on the problem of predicting production line status for Printed Circuit Boards (PCBs). The problem contains three prediction tasks regarding PCB producing process. Firstly, data exploration is carried out and it reveals several data challenges, including data imbalance, data noise, small sample size, and component difference. To predict production line status for components of PCBs using records of inspection on pins, we proposed two possible feature extraction methods to compress the pin-level data into component-level. A statistical feature extraction method, which retrieves descriptive statistics such as mean, standard deviation, maximum, and minimum of pins on the same component, is applied to Task 1, while a PinNumber-based feature extraction method, which keep original values for 2-pin components, is applied to Task3. In addition, a neural-net model with feeding imbalance control is established for Task 1. and a random forests model is applied for both Task 2 and Task 3. Moreover, a threshold moving technique is proposed to optimize the threshold selection. Finally, the result shows that our models achieved f1-scores of 0.44, 0.54, and 0.71 on the test set for the three tasks, respectively.