Printed Circuit Board Manufacturing Research Articles

The MAPS foil

We present a method of embedding a Monolithic Active Pixel Sensor (MAPS) into a flexible printed circuit board (FPC) and its interconnection by means of through-hole copper plating. The resulting assembly, baptised “MAPS foil”, is a flexible, light, protected, and fully integrated detector module. By using widely available printed circuit board manufacturing techniques, the production of these devices can be scaled easily in size and volume, making it a compelling candidate for future large-scale applications.A first series of prototypes that embed the ALPIDE chip has been produced, functionally tested, and shown to be working.

Identification of the Angle Errors of the LED Parallel-Light Module in PCB Exposure Device by Using Neural Network Learning Algorithms

For the smart manufacturing development of printed-circuit-board (PCB) exposure devices, the LED parallel-light (LPL) module is investigated and the angle errors of those LPL units are identified by neural network learning algorithms. At present, in PCB manufacturing, most circuit boards use photoresist covering etching. After exposure and development, unwanted copper foil is etched and removed to make circuit boards. The exposure process is its key process, and the equipment used in this process is an exposure machine. The LPL unit is designed and the LPL exposure module is searched under the principle of higher irradiance uniformity. The learning data of supervised learning for the convolutional neural network (CNN) include a 2D irradiance distribution image constructed by the ray tracing simulation tool. In these supervised learning data, all units of LPL-EM are randomly added with a self-specific angle error. By using Fast Region-based CNN, the identification of the multi-LPL module with the specific errors of inclination and azimuth angle is verified. Those results preliminarily illustrate that supervised learning techniques should be able to help identify the errors of inclination and azimuth angle for the single LPL unit and multi-light module of PCB exposure devices. In other words, this technology should serve as a reference for the development of the PCB exposure process towards smart manufacturing.

Semi-Supervised Defect Detection Method with Data-Expanding Strategy for PCB Quality Inspection.

Printed circuit board (PCB) defect detection plays a crucial role in PCB production, and the popular methods are based on deep learning and require large-scale datasets with high-level ground-truth labels, in which it is time-consuming and costly to label these datasets. Semi-supervised learning (SSL) methods, which reduce the need for labeled samples by leveraging unlabeled samples, can address this problem well. However, for PCB defects, the detection accuracy on small numbers of labeled samples still needs to be improved because the number of labeled samples is small, and the training process will be disturbed by the unlabeled samples. To overcome this problem, this paper proposed a semi-supervised defect detection method with a data-expanding strategy (DE-SSD). The proposed DE-SSD uses both the labeled and unlabeled samples, which can reduce the cost of data labeling, and a batch-adding strategy (BA-SSL) is introduced to leverage the unlabeled data with less disturbance. Moreover, a data-expanding (DE) strategy is proposed to use the labeled samples from other datasets to expand the target dataset, which can also prevent the disturbance by the unlabeled samples. Based on the improvements, the proposed DE-SSD can achieve competitive results for PCB defects with fewer labeled samples. The experimental results on DeepPCB indicate that the proposed DE-SSD achieves state-of-the-art performance, which is improved by 4.7 mAP at least compared with the previous methods.

MGPM Methods for Rectangular Single-Size Printed Circuit Board Orthogonal Packing Problems

Printed circuit board (PCB) orthogonal packing problems derive from the massive circuit board production process in the electronic industry. In the early stage, it mainly relied on human experiences to make decisions on how to make the most of each sheet while meeting customers’ orders. Up till now, researchers in enterprises and academic circles are still trying to explore effective mathematical models and feasible optimization methods. In most cases, what PCB companies generally face is the orthogonal layout decision, which considers positioning each rectangle PCB piece (PCB-P) on a rectangle sheet board (panel) in an ideal way so that each panel has the minimal remains with the most PCB-Ps. In this paper, multi-round gradual packing methods (MGPMs) are proposed based on the idea of combinatorial optimization and dynamic programming. MGPMs include the depth priority-based method (MGPM-DP) and breadth priority-based branch-and-prune method (MGPM-BC). The former has the advantage of finding better solutions, while the latter has the advantage of consuming short computational time. Through extensive computational tests on real data from a PCB production enterprise in China, both MGPM-DP and MGPM-BC have achieved noticeable and satisfactory results compared with the simplex method, dynamic programming method, and a widely used industrial software (Yuanbo) in China.

Perspectives and Impact of Green Electrodeposition

Green electrodeposition is a concept where electrodeposition is carried out sustainably; with safety, society, and environmental aspects playing a significant role in decision-making. ECS and electrochemists have seen large changes in the use of materials and chemicals over the last 50 years: for example, the slow phase out of mercury in labs and industry due to its toxicity [1]. On the other hand, the development of DSA and Boron-doped Diamond (BDD) electrodes have provided strategies for effluent control [2]. The Electrodeposition and Electroless Division (EDLP) members also would have faced challenges over the last 40 years. This could have involved replacing Cr(VI) electrolytes [3], Cd-based coatings, or developing cyanide-free processes [4]. In many cases EDLP has led the exploration of a new set of functional materials: an exemplar being “Get the Lead Out” Symposia Series focused on the development of Pb-free solders.Green Electrodeposition is a marriage of Green Chemical Principles and Electrodeposition. Green Chemical Principles [5], urges one to focus on the reduction of waste, atom economy, energy efficiency, benign solvents, etc. These guidelines are useful for the development of any process, including electrodeposition processes. To adopt these principles, one has to understand and consider the stability of chemical formulations [6], surface electrochemistry that enables electrodeposition [7] and energy and atom efficiency of the process [8]. Electrochemistry, in particular, is a low temperature, low pressure process using electrons to drive reactions which reduces waste. It should therefore follow, that electrochemistry has a significant advantage in offering “Green” processes if developed, implemented and monitored carefully.The talk will focus on how the concept started which simply focused on “prevention of generating” of waste. In particular it examined the notion of development of alternative formulations, the thermodynamics of stable electrolytes and associated issues. The application of this approach towards the development of gold deposition using non-cyanide baths for opto-electronic devices [9] will serve as an exemplar. Following this, the talk will focus effluent remediation of waste from printed circuit board manufacturing [10] and its extension to clean up of galvanic sludge from plating companies [11]. If time permits the use of engineering methodologies using current pulsing, novel agitation schemes and monitoring methods for energy efficiency and metal recycling and recovery will also be discussed.

Detection of Printed Circuit Board Defects on ENIG and ENIPIG Surface Finishes with Convolutional Neural Networks and Evaluation of Training Parameters

Increasingly high demands are being placed on the quality inspection of printed circuit boards (PCBs). A full surface inspection of all produced PCBs and a high defect detection accuracy of the inspection system are becoming prerequisites for an efficient quality management. At the same time, the demand for PCBs is constantly increasing over the years due to the high demand for electrical devices. Human inspection is no longer feasible due to the high production rates and required defect detection accuracy. Therefore, automatic inspection systems are increasingly used for quality control in the various process steps of PCB production. In this article, the first automatic inspection system for detecting defects on Electroless Nickel Immersion Gold (ENIG) and Electroless Nickel Immersion Palladium Immersion Gold (ENIPIG) surfaces is presented. A pretrained convolutional neural network (CNN) and the sliding window approach are used. A training dataset consisting of six different defect types and an OK class containing only defect-free PCB images was labeled for this classification problem. The hyperparameters learning rate and batch size are varied for different training runs of the CNN, and the performance of the network in PCB defect detection is evaluated using a test dataset. The true-positive rate, truenegative rate, and F1-score were analyzed for the evaluation. Our results show that the best performances could be achieved at low batch sizes and low learning rates.

Defect Detection in Printed Circuit Boards Using Leaky-LeNet 5

Each electronic device includes printed circuit boards (PCBs), where defect detection is an important process to enhance the quality of PCB production. To accomplish error-free PCBs, the researchers and experts converted traditional manual inspections into automated systems. The manual inspection results are ineffective, where the non-defective PCBs are classified as defective PCBs. A subsequent study added a technique called LeNetwork-5 (LeNet-5) and speeded up robust feature extraction (SURF) techniques to identify defects. The existing method was unreliable, so further research was conducted in this area using Leaky techniques. Two objectives are achieved using SURF technique: (1) registration of the PCB to be checked against a reference PCB and (2) identification of feature points on the PCB helps to find missing components. Additionally, the Leaky LeNet-5 classifier is employed to classify the defects in PCBs. The proposed method achieved accuracy of 99.83%, sensitivity of 96.26%, specificity of 99.15%, and F1-Score of 98.45%.

The use of synthetic agonists of quorum sensing N- acyl homoserine lactone pathway improves the bioleaching ability in Acidithiobacillus and Pseudomonas bacteria.

Metal solubilization from discarded electrical material and electronic devices (e-waste) using the bioleaching capabilities of bacterial cells is highly effective. However, gaps in understanding about the microbiological processes involved in the bioleaching reaction leads to less efficient metal solubilization in large-scale e-waste processing. In this study, bacterial species belonging to the genera Acidithiobacillus and Pseudomonas were used to leach copper and gold from discarded printed circuit boards (PCB). Through modulation of the cell-to-cell communication system in these bacteria, phenotypic traits directly involved in the bioleaching reaction were regulated in order to improve the metal solubilization. Addition of the long chain synthetic autoinducer molecule N-acyl homoserine lactone (AHL) of the quorum sensing pathway to the bioleaching reaction resulted in a significant enhancement of metal extraction from PCB. Factors such as: cell attachment to PCB, biofilm formation and hydrogen cyanide (HCN) production were regulated by the quorum sensing system and could be directly related to the improvement of metal bioleaching. Bioleaching reactions using bacterial quorum sensing modulation could represent a valuable tool in overcoming limitations at the industrial level imposed by microbiological traits that lead to inefficient metal bioleaching from e-waste.

Evaluation of thermal characteristics of laser induced heating of microfilm in relation to printed circuit board manufacturing

The emerging field of microelectronics has expeditiously developed over recent decades, owing to the development of novel materials and state-of-the-art fabrication technologies, as it offers crucial solutions to specific and complicated challenges. Laser systems are effectively utilized in material processing applications due to their inherent capabilities that provide rapid, quality and flexibility to reduce production costs involved. Microelectronics manufacturing basically comprises circuit boards (printed circuit boards), semiconductor, IC wafers are a few applications where laser system has been employed over traditional manufacturing technologies. In the viewpoint of the importance of the application of laser in the arena of microelectronics, the present research work is dedicated to evaluate the thermal characteristics developed in laser heating of microfilm (printed circuit board). During the operation of laser heating on the substrate surface, the quantity of material removal is largely dependent on the high temperature induced by the laser. The parametric laser-substrate interaction phenomena are the prime deciding factor for the successful accomplishment of the manufacturing operation. From the literature review, it has been observed that not much concentration has been given to the application of non-Fourier’s model though it is specifically suited for microscale heat transfer. This research paper theoretically investigates the thermal characteristics (peak temperature, temperature distribution curvature, pulse time, optical penetration depth, time of laser exposure, laser absorption radius and so on) by employing non-Fourier’s heat conduction model with pulse laser heat source. The theoretical estimation will be validated by the existing experimental research outcomes. The present research paper might be an asset for deciding the design of process variables and protocols in terms of laser-based manufacturing in the arena of microelectronics.

A Case study on Organizational Dysfunctions and Hidden cost in a PCB manufacturing company in China

The author proposed the specific case studies on the feasibility of the digital transformation implementation with the related technologies on the solution to the operational over-costs in the company, and then find out the specific problems that existed on the staffs’ communication in the crossed-departments, and explore the further correlation between the digital transformation and the solutions to the operational costs and the inefficient crossed-departments’ communication in the PCB (Printed Circuit Board) manufacturing company. The studies were conducted with the relevance of the theories “Organizational Dysfunctions” and “Hidden Costs”. Meanwhile, he implemented two research methods, which are respectively Correlational Research in quantitative and Semi-Structured Interviewing in qualitative in this study. The paper reveals the significance of digital transformation on the solution to operational over-costs and the inefficient crossed-departments communication through the identification from the analytical results of correlational research, and semi-structured interviews, where the analysis was conducted based on the data in the forms of company records (financial statements), and documentations (interviewing contents). Moreover, it indicates the strong correlation with the research implementation on the theory of Organizational Dysfunctions and Hidden Costs as well.

Design and Implementation of an HCPS-Based PCB Smart Factory System for Next-Generation Intelligent Manufacturing

The next-generation intelligent smart factory system that is proposed in this paper could improve product quality and realize flexible, efficient, and sustainable product manufacturing by comprehensively improving production and management innovation via its digital network and intelligent methods that reflect the characteristics of its printed circuit board (PCB) manufacturing design and on-site implementation. Intelligent manufacturing systems are complex systems that are composed of humans, cyber systems, and physical systems and aim to achieve specific manufacturing goals at an optimized level. Advanced manufacturing technology and next-generation artificial intelligence (AI) are deeply integrated into next-generation intelligent manufacturing (NGIM). Currently, the majority of PCB manufacturers are firms that specialize in processing orders from leading semiconductor and related product manufacturers, such as Samsung Electronics, TSMC, Samsung Electro-Mechanics, and LG Electronics. These top companies have been responsible for all product innovation, intelligent services, and system integration, with PCB manufacturers primarily playing a role in intelligent production and system integration. In this study, the main implementation areas were divided into manufacturing execution system (MES) implementation (which could operate the system using system integration), data gathering, the Industrial Internet of Things (IIoT) for production line connection, AI and real-time monitoring, and system implementation that could visualize the collected data. Finally, the prospects of the design and on-site implementation of the next-generation intelligent smart factory system that detects and controls the occurrence of quality and facility abnormalities are presented, based on the implementation system.

Effects of Coating Parameters of Hot Filament Chemical Vapour Deposition on Tool Wear in Micro-Drilling of High-Frequency Printed Circuit Board

High-frequency and high-speed printed circuit boards (PCBs) are made of ceramic particles and anisotropic fibres, which are difficult to machine. In most cases, severe tool wear occurs when drilling high-frequency PCBs. To protect the substrate of the drills, diamond films are typically fabricated on the drills using hot filament chemical vapour deposition (HFCVD). This study investigates the coating characteristics of drills with respect to different HFCVD processing parameters and the coating characteristics following wear from machining high-frequency PCBs. The results show that the methane concentration, processing time and temperature all have a significant effect on the grain size and coating thickness of the diamond film. The grain size of the film obviously decreases as does the methane concentration, while the coating thickness increases. By drilling high-frequency PCBs with drills with nanocrystalline and microcrystalline grain sizes, it is discovered that drills with nanocrystalline films have a longer tool life than drills with microcrystalline films. The maximum length of the flank wear of the nanocrystalline diamond-coated drill is nearly 90% less than microcrystalline diamond-coated tools. Moreover, drills with thinner films wear at a faster rate than drills with thicker films. The findings highlight the effects of HFCVD parameters for coated drills that process high-frequency PCBs, thereby contributing to the production of high quality PCBs for industry and academia.

End-to-end deep learning framework for printed circuit board manufacturing defect classification

We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed Circuit Board (PCBs). We describe the complete model architecture and compare with the current state-of-the-art using the same PCB defect dataset. These benchmark methods include the Faster Region Based Convolutional Neural Network (FRCNN) with ResNet50, RetinaNet, and You-Only-Look-Once (YOLO) for defect detection and identification. Results show that our method achieves a 98.1% mean average precision(mAP[IoU = 0.5]) on the test samples using low-resolution images. This is 3.2% better than the state-of-the-art using low-resolution images (YOLO V5m) and 1.4% better than the state-of-the-art using high-resolution images (FRCNN-ResNet FPN). While achieving better accuracies, our model also requires roughly 3× fewer model parameters (7.02M) compared with the state-of-the-art FRCNN-ResNet FPN (23.59M) and YOLO V5m (20.08M). In most cases, the major bottleneck of the PCB manufacturing chain is quality control, reliability testing and manual rework of defective PCBs. Based on the initial results, we firmly believe that implementing this model on a PCB manufacturing line could significantly increase the production yield and throughput, while dramatically reducing manufacturing costs.

Tetrabromobisphenol A (TBBPA) generation and removal paths analysis in printed circuit board (PCB) industrial wastewater: Lab-scale investigations

With the rapid development of printed circuit board (PCB) industry, the problem of the release of tetrabromobisphenol A (TBBPA) during PCB manufacturing processes was recognized and harmful to the receiving water body. In this study, TBBPA generation paths, fates in industrial wastewater in the PCB manufacturing processes were analyzed. Gas chromatograph-mass spectrometer (GC-MS) scanning results showed that the raw materials including solder mask ink, character ink and dry film mainly contained TBBPA. The PCB fabrication procedures, mainly including development, etching, stripping of inner layer manufacturing process, and pattern plating, tin-plating of outer layer manufacturing process, and development during the forming and surface treatment process would introduce TBBPA into wastewater. Flocculation and activated sludge process (ASP) that were commonly used for industrial wastewater treatment were selected to assess their removal capability for TBBPA in the wastewater, both which could remove more than 80% TBBPA. Low pH below 7, increased PAC and PAM dosages, and proper dosing during the slow-stirring stage could enhance the TBBPA removal of flocculation-sedimentation process. The ASP removes TBBPA mainly by the path of adsorption. High activated sludge concentration, low pH, medium-temperature and limited competitive adsorbates (NH4+, Cu2+) were beneficial to remove TBBPA by the ASP.

Preferential removal of aromatics-dominated electronic industrial emissions using the integration of spray tower and photocatalysis technologies

Industrial emissions from the manufacturing of printed circuit boards (PCBs) are characterized by low concentration and high gas flow rates, and are one of the important sources of toxic and odorous volatile organic compounds (VOCs). They are deleterious to human health and considered as important precursors for the formation of ozone. Herein, we conducted a case study in which an integrated decontamination technique consisting of a spray tower (ST) and photocatalysis (PC) was applied to purify the waste gas emitted from a PCB manufacturing facility. Total of 66 VOCs including halogenated hydrocarbons (HHs), aromatic hydrocarbons (AHs), oxygen-containing hydrocarbons (OVOCs), and aliphatic hydrocarbons (AIHs) were quantified using GC-MS during four sampling events. The results showed that AHs were the largest group (contributing >85.0% of the total VOCs). The average removal efficiency (RE) of VOCs during the nine months’ continuous treatment was 72.39%. High RE was attributed to the following three aspects: the capture efficiencies of HHs with slight water solubility (50.16% of RE) and AIHs with low saturation pressure (54.75% of RE) were improved by the process consisting of ST upstream of PC, whereas AHs was preferentially degraded by PC (61.34% of RE). In addition, the ozone formation potential and the health risk (cancer and non-cancer risks) greatly decreased after the ST-PC treatment. This study suggests that ST-PC technique is a promising approach for removing continuously emitted organic waste gas with low concentration and high gas flow rates, as well as reducing the ozone formation and risk of health hazards.

A Novel Data Mining Approach for Defect Detection in the Printed Circuit Board Manufacturing Process

Abstract This research aims to propose an effective model for the detection of defective Printed Circuit Boards (PCBs) in the output stage of the Surface-Mount Technology (SMT) line. The emphasis is placed on increasing the classification accuracy, reducing the algorithm training time, and a further improvement of the final product quality. This approach combines a feature extraction technique, the Principal Component Analysis (PCA), and a classification algorithm, the Support Vector Machine (SVM), with previously applied Automated Optical Inspection (AOI). Different types of SVM algorithms (linear, kernels and weighted) were tuned to get the best accuracy of the resulting algorithm for separating good-quality and defective products. A novel automated defect detection approach for the PCB manufacturing process is proposed. The data from the real PCB manufacturing process were used for this experimental study. The resulting PCALWSVM model achieved 100 % accuracy in the PCB defect detection task. This article proposes a potentially unique model for accurate defect detection in the PCB industry. A combination of PCA and LWSVM methods with AOI technology is an original and effective solution. The proposed model can be used in various manufacturing companies as a postprocessing step for an SMT line with AOI, either for accurate defect detection or for preventing false calls.

Analysis of Uncertainties in Inductance of Multi-Layered Printed-Circuit Spiral Coils.

Eddy-current sensors are widely used for precise displacement sensing and non-destructive testing. Application of printed-circuit board (PCB) technology for manufacturing sensor coils may reduce the cost of the sensor and enhance the performance by ensuring consistency. However, these prospects depend on the uniformness of the sensor coil. Inductance measurements of sample coils reveal rather considerable variations. In this paper, we investigate the sources of these variations. Through image analysis of cut-away cross-sections of sensor coils, four factors that contribute to the inductance variations are identified: the distance between layers, the distance between tracings, cross-sectional areas, and misalignment among layers. By using and extending existing method of calculating inductance of spiral coils, the inductance distributions are obtained when these factors are randomly varied. A sensitivity analysis shows that the inductance uncertainty is most affected by the uniformness of the spacings between coil traces and the distances between layers. Improvements in PCB manufacturing process can help to reduce the uncertainty in inductance.

Equipment procurement for printed circuit board manufacturing using the analytic hierarchy process

For optimal equipment procurement in Taiwan, first, the specifications and prices of a product are evaluated. Based on these evaluations, procurement decisions are made. Ensuring accurate evaluation criteria for procurement is a multicriteria decision making (MCDM) problem. Printed circuit board (PCB) manufacturers have not grasped the decision-making factors of the new equipment specification, and they continue to use the previous generation specifications as the decision-making criteria for new equipment; this results in inappropriate evaluation criteria. When new equipment with different specifications is launched, purchasers may still use the specifications of older equipment as a basis for their purchase decisions. For example, thermal power generation specifications are often used to evaluate equipment for nuclear power generation. The difference in criteria renders it difficult to make decisions for new equipment. We proposed the use of a scientific method to establish a set of criteria decision-making models for equipment procurement to help PCB factories accurately evaluate contemporary specifications. This research established criteria for new technologies to help decision makers select the most appropriate method. We used the Delphi method and the analytic hierarchy process (AHP) to provide insights into technological and commercial requirements, and a model was further developed for optimal equipment procurement.

Cost estimation of photopolymer resin for 3D exposure of circuit boards

The research object in the work is the printed circuit boards (PCB) Production technological process using the additive technology of photopolymer 3D printing. The existing problem is that the manufacturing process of single-sided and double-sided PCBs, simple in technology, from the third to the fifth accuracy class, requires the use of a large amount of consumables and technological equipment. In turn, this affects the cost of the product. The research subject is models and methods for manufacturing PCB using photopolymer 3D printing. In order to reduce the cost of materials: film or aerosol photoresist, as well as reduce the number of technological operations, applying photoresist and for the manufacture of PCBs stencils, it is proposed to use photopolymer 3D printing technologies for the manufacture of PCBs. The paper analyzes the costs of Plexiwire Resin Basic Orange Transparen photopolymer resin for the manufacture of single-sided PCBs and calculates the cost of the consumable (resin) compared to the costs of dry film photoresist. 60 % cost of consumables (photopolymer resin) compared to dry film photoresist for making single-sided PCBs. The work is aimed at determining the dependence of the geometric dimensions of the PСВs topology and the consumption of photopolymer resin on the technological parameters of photopolymer exposure. A regression correlation model of the dependence of resin consumption on exposure parameters has been developed and correlation coefficients have been calculated. It has been established that with an increase in the exposure time of the photopolymer resin, the consumption of the photopolymer resin increases and the deviation of the geometric dimensions of the PCBs topology increases, which in turn negatively affects the quality of the product. Therefore, using the obtained regression model, it is possible to calculate the influence of parameters on the PCB topology and reduce the deviation of conductor sizes and resin consumption.

Advantages and Development Prospects of Automation in Printed Circuit Board Manufacturing

Advantages and Development Prospects of Automation in Printed Circuit Board Manufacturing