Printed Circuit Board Research Articles

SDD-Net: Soldering defect detection network for printed circuit boards

The rapid detection of soldering defects in printed circuit boards (PCBs) is crucial and a challenge for quality control. Thus, a novel soldering defect detection network (SDD-Net) is proposed based on improvements in YOLOv7-tiny. A fast spatial pyramid pooling block integrating a cross-stage partial network is designed to expand the receptive field and feature extraction ability of the model. A hybrid combination attention mechanism is proposed to boost feature representation. A residual feature pyramid network is subsequently presented to reinforce the capability of multilevel feature fusion to overcome the scale variance issue in PCB soldering defects. Finally, efficient intersection over union loss is applied for bounding box regression to accelerate model convergence while improving localisation precision. SDD-Net achieves a stunning mean average precision of 99.1% on the dataset, producing a 1.8% increase compared with the baseline. The detection speed is boosted to 102 frames/s for input images of 640 × 640 pixels using a mediocre processor. In addition, SDD-Net exhibits outstanding generalisation ability in two public surface defect datasets.

A deep learning approach for automated PCB defect detection: A comprehensive review

The ever-expanding market for electronic devices has significantly heightened the demand for high-quality printed circuit boards (PCBs). Even minor defects in PCBs can pose substantial safety risks for end-users. This article provides a comprehensive review on deep learning-based approaches for PCB defect detection. Our exploration covers various critical aspects, including the classification of PCB defects, automated vision inspection (AVI) techniques, object detection methodologies, and the widespread adoption of deep learning models. Specifically, we focus on the state-of-the-art approach known as region-based Fully Convolutional Network with Feature Pyramid Networks (FPN-RFCN). Additionally, we discuss effective data augmentation techniques and commonly used evaluation metrics in this domain. This review provides valuable insights for researchers, practitioners, and industry professionals engaged in PCB quality assurance.

Self-calibratable absolute modular rotary encoder: a theoretical feasibility study

Abstract This paper introduces a self-calibratable absolute modular rotary encoder based on the Equal Division Average (EDA) method, designed to significantly enhance measurement accuracy and simplify installation. The proposed design integrates multiple optical sensors into the encoder stator Printed Circuit Board (PCB), enabling precise absolute position measurement through the averaging of sensors data. This multi-sensor approach compensates for alignment and installation inaccuracies, which are common issues in conventional modular encoders. To validate the design and predict encoder behaviour prior to manufacturing, a theoretical modelling of virtual optical sensors is performed. Based on experimentally collected cross-calibration data from a real optical encoder, this modelling framework enables to estimate the efficiency of self-calibration algorithm with sufficient accuracy and optimize the design and performance of the encoder. The obtained results confirm that the proposed measurement system significantly reduces error margin, improving the reliability and precision of position feedback. The initial position deviation of several hundreds of arcseconds might be reduced and kept below 8 arcseconds by using two or more additional optical sensors, even with the 0.5 mm misalignment of the encoder stator.

Enhanced Corrosion Protection of Printed Circuit Board Electronics using Cold Atmospheric Plasma-Assisted SiOx Coatings.

The miniaturization and widespread deployment of electronic devices across diverse environments have heightened their vulnerability to corrosion, particularly affecting copper traces within printed circuit boards (PCBs). Conventional protective methods, such as conformal coatings, face challenges including the necessity for a critical thickness to ensure effective barrier properties and the requirement for multiple steps of drying and curing to eliminate solvent entrapment within polymer coatings. This study investigates cold atmospheric plasma (CAP) as an innovative technique for directly depositing ultrathin silicon oxide (SiOx) coatings onto copper surfaces to enhance corrosion protection in PCBs. A systematic investigation was undertaken to examine how the scanning speed of the CAP deposition head impacts the film quality and corrosion resistance. The research aims to determine the optimal scanning speed of the CAP deposition head that achieves complete surface coverage while promoting effective cross-linking and minimizing unreacted precursor entrapment, resulting in superior electrical barrier and mechanical properties. The CAP coating process demonstrated the capability of depositing SiOx onto copper surfaces at various thicknesses ranging from 70 to 1110 nm through a single deposition process by simply adjusting the scanning speed of the plasma head (5-75 mm/s). Evaluation of material corrosion barrier characteristics revealed that scanning speeds of 45 mm/s of the plasma deposition head provided an effective coating thickness of 140 nm, exhibiting superior corrosion resistance (30-fold) compared to that of uncoated copper. As a proof of concept, the efficacy of CAP-deposited SiOx coatings was demonstrated by protecting an LED circuit in saltwater and by coating printed circuits for potential agricultural sensor applications. These CAP-deposited coatings offer performance comparable to or superior to traditional conformal polymeric coatings. This research presents CAP-deposited SiOx coatings as a promising approach for effective and scalable corrosion protection in miniaturized electronics.

Stretchable Arduinos embedded in soft robots.

To achieve real-world functionality, robots must have the ability to carry out decision-making computations. However, soft robots stretch and therefore need a solution other than rigid computers. Examples of embedding computing capacity into soft robots currently include appending rigid printed circuit boards to the robot, integrating soft logic gates, and exploiting material responses for material-embedded computation. Although promising, these approaches introduce limitations such as rigidity, tethers, or low logic gate density. The field of stretchable electronics has sought to solve these challenges, but a complete pipeline for direct integration of single-board computers, microcontrollers, and other complex circuitry into soft robots has remained elusive. We present a generalized method to translate any complex two-layer circuit into a soft, stretchable form. This enabled the creation of stretchable single-board microcontrollers (including Arduinos) and other commercial circuits (including SparkFun circuits), without design simplifications. As demonstrations of the method's utility, we embedded highly stretchable (>300% strain) Arduino Pro Minis into the bodies of multiple soft robots. This makes use of otherwise inert structural material, fulfilling the promise of the stretchable electronic field to integrate state-of-the-art computational power into robust, stretchable systems during active use.

Impact of decoupling capacitor aging and temperature for the long-term reliability of power delivery networks

Nowadays, almost all electronic systems on printed circuit boards (PCB) adopt a vital element known as the power delivery network (PDN). However, the performance of the PDN is susceptible to variables such as the temperature and aging of its key constituents: the decoupling capacitors (decaps). Consequently, the long-term reliability of the PDN demands meticulous consideration to foresee how its performance can deviate from the initial design specifications. A realistic high-current server system is considered. It involves hundreds of decaps to achieve the required target impedance as optimally selected and laid out by the Power Integrity (PI) designer. The degradation of decap performance is analyzed by collecting experimental data from tens of decaps for each type used in the design while applying an accelerated aging process at different temperatures. The impact of aging in terms of the capacitance, parasitic inductance, and resistance of the decaps is considered to illustrate an innovative methodological design approach based on statistical analysis. Such a design approach can prevent the detrimental impact of a larger noise level due to the gradual performance degradation of the PDN over the intended life cycle of the system.

PCB defect detection algorithm based on deep learning

Deep learning gained great popularity in the task of object detection. This paper proposes a printed circuit board (PCB) defect detection algorithm based on deep learning, which can improve product quality and avoid potential failures and accidents in the electronics manufacturing industry. In this paper, the YOLOv7 model is selected as the original model for PCB defect detection. Firstly, the K-means++ clustering algorithm is used to calculate the target anchor parameters which can enhance the dataset. Secondly, the receptive field enhancement (RFE) module is added to the head layer of the network to take full advantage of the receptive field in the feature map. Thirdly, the loss function CIoU of the YOLOv7 model is changed to WIoUv2. Fourthly, add the Triplet attention mechanism to the CBS and SPPCSPC modules. Finally, the detection accuracy of the improved YOLOv7 model is compared with that of Faster R-CNN, SSD, YOLOv3-tiny, YOLOv5s, and YOLOv7 models. The experimental results show that the detection accuracy and detection speed of the improved YOLOv7 model are enhanced compared with the original YOLOv7 model.

Signal Processing Method to Synthesize Eddy Current Sensors

Abstract This paper focuses on the development of a novel method for synthetizing an eddy current sensor using post-processing techniques based on the linear superposition of multiple parallel acquisition signals. The proposed method will be demonstrated in the context of a cross-wound sensor (CWS) made of a flexible printed circuit board (PCB). Traditionally, the configuration and detection orientation of an eddy current sensor are determined during the fabrication process, which limits the flexibility and versatility of the sensor. However, the method developed in this research enables the post-processing fabrication of the sensor, increasing the customization potential and adaptability. Furthermore, the proposed method also enables the production of a suitable lift-off sensor (LOS) for dynamic lift-off compensation (DLOC). DLOC is a crucial aspect of eddy current sensing as it helps to account for the distance between the sensor and the target surface, which can affect measurement accuracy. The ability to configure the CWS detection orientation with DLOC capabilities in post-processing offers significant advantages in terms of sensor performance and versatility. Experimental results will be presented that demonstrate the effectiveness of the proposed method in enabling post-processing fabrication of an eddy current sensor featuring configurable detection orientation and DLOC capabilities.

MFAD-RTDETR: A Multi-Frequency Aggregate Diffusion Feature Flow Composite Model for Printed Circuit Board Defect Detection

To address the challenges of excessive model parameters and low detection accuracy in printed circuit board (PCB) defect detection, this paper proposes a novel PCB defect detection model based on the improved RTDETR (Real-Time Detection, Embedding and Tracking) method, named MFAD-RTDETR. Specifically, the proposed model introduces the designed Detail Feature Retainer (DFR) into the original RTDETR backbone to capture and retain local details. Subsequently, based on the Mamba architecture, the Visual State Space (VSS) module is integrated to enhance global attention while reducing the original quadratic complexity to a linear level. Furthermore, by exploiting the deformable attention mechanism, which dynamically adjusts reference points, the model achieves precise localization of target defects and improves the accuracy of the transformer in complex visual tasks. Meanwhile, a receptive field synthesis mechanism is incorporated to enrich multi-scale semantic information and reduce parameter complexity. In addition, the scheme proposes a novel Multi-frequency Aggregation and Diffusion feature composite paradigm (MFAD-feature composite paradigm), which consists of the Aggregation Diffusion Fusion (ADF) module and the Refiner Feature Composition (RFC) module. It aims to strengthen features with fine-grained awareness while preserving a certain level of global attention. Finally, the Wise IoU (WIoU) dynamic nonmonotonic focusing mechanism is used to reduce competition among high-quality anchor boxes and mitigate the effects of the harmful gradients from low-quality examples, thereby concentrating on anchor boxes of average quality to promote the overall performance of the detector. Extensive experiments are conducted on the PCB defect dataset released by Peking University to validate the effectiveness of the proposed model. The experimental results show that our approach achieves the 97.0% and 51.0% performance in mean Average Precision (mAP)@0.5 and mAP@0.5:0.95, respectively, which significantly outperforms the original RTDETR. Moreover, the model reduces the number of parameters by approximately 18.2% compared to the original RTDETR.

High-Accuracy, Vision-Based Attitude Estimation System for Air-Bearing Spacecraft Simulators

Air-bearing platforms for simulating the attitude dynamics of satellites require highly accurate ground truth systems. Commercial off-the-shelf motion-capture systems are used for this scope, although they are complex and expensive. This paper shows a novel and versatile method to estimate the attitude of rotational air-bearing platforms using a monocular camera and sets of fiducial LED markers. The work proposes a geometry-based, iterative algorithm that is significantly more accurate than other literature methods that involve solving the perspective-n-point problem. Additionally, autocalibration procedures to perform a preliminary estimation of the system parameters are shown. The developed methodology is deployed onto a Raspberry Pi 4 microcomputer and tested with an array of LEDs soldered on a custom printed circuit board. Data obtained with this setup are compared against simulations of the system to determine and validate its performance. The hardware setup is also validated against independent inclinometric and gyroscope sensor measurements. Simulation and test results show expected 1-sigma accuracies of 12 arcs and 37 arcs for about- and cross-boresight rotations of the platform, respectively, and average latency times of 6 ms.

ALLEGRO FCC-ee detector concept & Noble liquid calorimetry

ALLEGRO is a proposed FCC-ee general-purpose detector concept with a noble liquid (NL) electromagnetic calorimeter (ECAL) as a central feature. Calorimetry based on liquified noble gases is a well-proven technology that has been successfully applied in numerous high-energy physics (HEP) experiments. This technology provides excellent energy resolution, linearity, stability, uniformity and timing properties at a reasonable cost, making it a strong candidate for future HEP experiments. By using multi-layer printed circuit boards as readout electrodes, we can build a calorimeter with almost arbitrarily high granularity. This in turn allows for four-dimensional imaging, machine learning algorithms and particle-flow reconstruction to be fully exploited. In this proceedings contribution we give an overview to the ALLEGRO concept and present the ongoing R&D work for adapting NL calorimetry to an ECAL of a lepton collider experiment. In addition to simulation studies and expected performance, we show results on signal extraction and noise mitigation studies made with readout electrode prototypes and compare the measurements to simulations, as well as discuss test results of absorber prototypes. In addition we present progress of the mechanical design project and status of the barrel ECAL test-beam prototype module development.

Synchronisation of thermal imaging and multi-channel EIS to interpret planar array PCB fuel cell performance

The planar design of printed circuit board (PCB) fuel cells offers the advantages of simple structure, rapid prototyping and easy manufacturing. However, uneven reactants and heat distribution in each PCB cell module often result in inconsistent overall performance. Current characterisation methods mainly focus on the study of the overall performance and lack the analysis of individual cells, which provide a limited understanding of localised electrochemical and thermal effects that might play a critical part in further optimising and diagnosing the cell performance of this category. We have developed a multi-channel impedance diagnostic technique for PCB fuel cells. Combining with thermal imaging, we could simultaneously diagnose individual open cathode cell modules by measuring the heat distribution and electrochemical impedance spectra (EIS) of 11 PCB cell modules. Hence, the correlation between heat distribution, internal resistance and performance of different sections of PCB fuel cells can be established. By comparing the fuel cell’s pristine and degraded, we demonstrated how this non-destructive technique could identify and locate cell modules with deteriorated performance, which would be beneficial to real-time diagnostics, quality control and optimisation. The multi-channel impedance measurement takes several seconds only through optimised multi-frequency perturbation and can be readily extended to stacks containing more cells by simply increasing the number of voltage sensors, which can be further adapted to other electrochemical devices.

A miniaturized on‐chip BPF with low insertion loss and wide stopband based on integrated passive device technology

AbstractIn this study, a novel bandpass filter (BPF) characterized by low insertion loss (IL) and the presence of two transmission zeros (TZs) based on integrated passive device (IPD) technology is introduced. The design incorporates a low‐pass filter and a high‐pass filter which both exhibit strong out‐of‐band suppression performance. The control structure for TZs is constructed by cascading the inductance and capacitance components. The TZ controlling structure primarily generates TZs at high frequencies, resulting in a further bandwidth enhancement in the stopband. The lumped circuit of the proposed BPF is first constructed, and rigorous design formulas are provided to assist in constructing the proposed design. After schematic optimization, the second step involves layout optimization and simulation based on the specific IPD process. Experimental results mounted on a printed circuit board demonstrate that the bandwidth spans from 3.3 to 4.2 GHz with an IL of only 2.0 dB at the center frequency. Additionally, this BPF achieves impressive sideband suppression, exceeding 18 dB in the 0–1.7 GHz range and 30 dB in the 9–15 GHz range. Remarkably, the BPF is exceptionally compact with only 0.5 mm × 1.0 mm (0.006λc × 0.012λc). The proposed BPF exhibits outstanding performance with minimal IL and extensive sideband suppression at such a compact size based on the IPD technology.

Detecting Manufacturing Defects on Printed Circuit Boards Using Metamaterial-Based Circular Microstrip Patch Antenna

In this study, a microstrip circular patch antenna is designed having a metamaterial-based (MTM) ground plane to detect manufacturing defects of short circuits and open circuits on printed circuit boards (PCB). In that regard, PCB specimens of FR-4 as the substrate and copper microstrip lines as the conductive wiring lines are designed and manufactured. Five vertical copper lines printed side by side on the top of the substrate are used to demonstrate the working mechanism of the designed antenna sensor. Two different defect scenarios of open and short circuits with controlled locations are studied to determine variations in the return loss data of the proposed structure. MTM cell structures with cross lines enclosed by an octagon ring are periodically placed as part of the ground plane of the proposed antenna to obtain higher sensitivity of the designed and manufactured sensor. Antenna return loss behaviors in terms of the locations of the faults are employed to prepare a database to detect not only the presence of the faults but also determine their locations. Since the samples to be measured are not irreversibly damaged during the testing process, the proposed design can be considered a non-destructive measurement method to provide information about the type and location of defects with real-time measurement data.

Design and research of multi-information fusion RFID sensor for fruit and vegetable quality detection.

With the increasing demand for fruits and vegetables in the market, the development of cold chain logistics has put forward higher requirements for the quality of fruits and vegetables in storage. To ensure the freshness of fruits and vegetables during storage and transportation and avoid unnecessary loss, it is necessary to conduct real-time detection of their odor to ensure their quality. Therefore, based on nano-composite materials combined with Radio Frequency Identification (RFID) technology, this paper designs an integrated RFID sensor that can simultaneously detect temperature, carbon dioxide, and ethanol concentrations. The test results show that the sensor has a high sensitivity of 0.25dB/°C, 0.011dB/ppm, and 0.65MHz/ppm for detecting temperature, carbon dioxide, and ethanol concentration, respectively. The sensor also uses Printed Circuit Board (PCB) technology to make the sensor base, which has the advantages of low cost, easy portability, and mass production capability. The results obtained evidencethat the system meets the requirements of environmental monitoring for fruit and vegetable storage, runs stably, and has a high use value.

Electrochemical migration and dendrite growth between two electrodes: Experiments and Brownian dynamics simulations

Electrochemical migration (ECM) is a common cause of failure in printed circuit boards (PCB) due to dendrite growth between exposed metallic conductors under moisture especially in the presence of flux residues. Understanding this phenomenon, therefore, is of interest from technological and economical points of view. Here, we report experimental results of water drop tests on surfaces contaminated by various amount of flux residues, which are supplemented with Brownian dynamics (BD) results to gain molecular insight into ECM and dendrite growth. BD is a particle simulation method where the ions are modeled explicitly. We show dendrite growth curves (the length of the longest dendrite as a function of time) obtained from visual examination with digital video microscope and from BD simulations. Results for the time to failure are also shown for various values of the electric field strength and cation (Sn2+) concentration (dilution of contamination). We report a mapping of experimental and simulation results that is necessary due to the large concentrations and electric field strengths used in the BD simulations.

Unsupervised selective labeling for semi-supervised industrial defect detection

In industrial detection scenarios, achieving high accuracy typically relies on extensive labeled datasets, which are costly and time-consuming. This has motivated a shift towards semi-supervised learning (SSL), which leverages labeled and unlabeled data to improve learning efficiency and reduce annotation costs. This work proposes the unsupervised spectral clustering labeling (USCL) method to optimize SSL for industrial challenges like defect variability, rarity, and complex distributions. Integral to USCL, we employ the multi-task fusion self-supervised learning (MTSL) method to extract robust feature representations through multiple self-supervised tasks. Additionally, we introduce the Enhanced Spectral Clustering (ESC) method and a dynamic selecting function (DSF). ESC effectively integrates both local and global similarity matrices, improving clustering accuracy. The DSF maximally selects the most valuable instances for labeling, significantly enhancing the representativeness and diversity of the labeled data. USCL consistently improves various SSL methods compared to traditional instance selection methods. For example, it boosts Efficient Teacher by 5%, 6.6%, and 7.8% in mean Average Precision(mAP) on the Automotive Sealing Rings Defect Dataset, the Metallic Surface Defect Dataset, and the Printed Circuit Boards (PCB) Defect Dataset with 10% labeled data. Our work sets a new benchmark for SSL in industrial settings.

3D printing birdhouses with ceramic clay using a six-axis palletizing robot

Over the past few decades, billions of adult breeding birds have been lost worldwide. The primary factors for this population loss are habitat loss and global warming. To aid the efforts for bird conservation, we have designed a simple birdhouse that can be 3D printed rapidly using a six-axis arm robot. We transformed a six-axis palletizing robotic arm into a ceramic 3D printer with the help of an extruder, stepper motor, and a custom Printed Circuit Board (PCB) consisting of an ESP32 and a motor driver. The ceramic clay is stored in a pneumatic pressure tank. An air compressor pump transports the clay from the tank to the extruder. We conducted various experiments at 8–10 bars of air pressure with different water-to-clay ratios. We found that the optimum air pressure, water-to-clay ratio, and nozzle sizes are 10 bars, 0.084, and 5 mm, respectively. After determining the appropriate printing parameters, we designed the birdhouse in the Mastercam software and generated the G-code. The G-code is loaded onto the robot controller, and the birdhouse is printed in parts. We dried the birdhouse for 24 h and then baked it in a kiln for 24 h at 1000 degrees Celsius. We then compared the ceramic birdhouse to a wooden birdhouse. The ceramic birdhouse maintains internal temperatures of 38 degrees Celsius or lower despite external temperatures reaching 43 degrees Celsius, which is on par if not better than the wooden birdhouse. This temperature will be beneficial for nesting, breeding, and egg development.

Neutron-based analytical techniques for the elemental composition analysis of electronic waste samples: advantages and challenges

Recycling electronic waste (WEEE) is a fundamental aspect of the circular economy. To develop technology and assess its economic aspects, the composition characterization of the waste items is crucial. Neutron-based analytical techniques, such as the INAA, in-beam NAA, and PGAA, are applicable to measure the elemental composition of WEEE. These techniques are bulk representative and offer simultaneous determination of the relevant elements without sample dissolution. This study explores the suitability of neutron-based elemental analysis methods for measuring common WEEE items, e.g., printed circuit boards and integrated circuits. Matrix effects mostly related to the thermal neutron self-shielding, were identified and successfully corrected, ensuring accurate mass fraction measurements.

Two-phase active immersion cooling for vertically mounted electronics with interchip component-assisted bubble departure

To address growing heat dissipation demands in high-performance computing chips, there is a transition from conventional cooling methods to advanced two-phase immersion cooling techniques for electronics thermal management. Here, we present a numerical investigation into the fluid dynamics and heat transfer in two-phase immersion cooling systems with vertically mounted chips on a printed circuit board (PCB) with interchip components such as capacitors, switches, and inductors. Leveraging a combined phase tracking and volume of fluid model, we study how the physical layout and geometries of interchip components affect the flow paths of bubbles, bubble coalescence phenomena, vapor coverage on chip surfaces, and cooling rates of heated chips. In an optimal PCB topology, a maximum heat flux of 18 W/cm2 and a heat transfer coefficient of 9650 W/m2K are observed for dielectric hydrofluoroether (HFE) - 7100 at an excessive wall temperature of 20 K under a constant wall temperature boundary condition. The research offers insights into the electronic system design to achieve efficient cooling for high-performance computing applications, demonstrating the critical role of interchip components in heat transfer.