Printed Circuit Board Industry Research Articles

Advanced AI Technologies for Defect Prevention and Yield Optimization in PCB Manufacturing

The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste. Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.

Solving line balancing and AGV scheduling problems for intelligent decisions using a Genetic-Artificial bee colony algorithm

Due to the rapid advancement of technology, the demand for electronic devices in various sectors such as consumer electronics, automotive, telecommunications, healthcare, and industrial applications, as well as customized printed circuit board (PCB) products, has significantly increased. Balancing the PCB assembly line is crucial for improving productivity to meet market demand, which means balancing the workload distributed among various assembly stations on the assembly line. However, the workload balancing depends on material-handling systems in production environments that facilitate the transportation of raw materials to the buffer storage of the assembly stations. In the PCB assembly system, the material handling is carried out using automated guided vehicles (AGVs). This paper studies the assembly line balancing and AGV scheduling collectively because of their dependency on each other. A mixed integer linear programming (MILP) model is formulated to balance the workload distribution among stations and schedule the AGVs. An existing intelligent platform of the real-life PCB industry is considered to solve this integrated problem with intelligent decisions. The platform includes three layers modules: physical layer, data management layer and application service layer. A novel genetic artificial bee colony (GABC) algorithm is proposed and embedded with an application service layer to optimize the current problem and give optimum solutions for the real-life physical layer. The proposed GABC algorithm incorporates the operators of the genetic algorithm (GA) i.e., crossover and mutation into the search process of the ABC algorithm. Additionally, the greedy selection feature is employed in GABC which significantly enhances the exploitation capabilities, leading to faster convergence, higher-quality solutions, and improved robustness. The performance of the proposed GABC algorithm is tested based on different parameters by comparing the results with GA, ABC and PSO algorithms. Experimental analysis indicates that the proposed GABC algorithm outperforms the compared algorithms. Therefore, the yielded solutions in terms of prompt responses help managers to expedite the production through intelligent decision-making based on customer demands.

Evaluating Performance Incorporating Multi-Criteria Decision Making and ESG Index: The Case of Taiwanese PCB Industry

The Printed Circuit Board (PCB) industry is known as the "mother of electronic system product" in the high-tech electronics industry. When this industry enters a mature stage, cost control becomes the key to its success. Additionally, as the corporate’s awareness of social responsibility like ESG (Environment, Social, & Governance) has risen, the cost control remains a critical indicator for evaluating a company's performance. This study uses the consolidated financial statements of 35 companies in Taiwan's listed PCB industry through collecting data from the Taiwan Stock Exchange from 2022 to 2022. This study chooses 16 key financial indicators through expert opinions and selects 7 input variables and 5 output variables from 5 screening criteria. Then, through the normalization of grey relational data, the degree of influence of each indicator on the performance of PCB manufacturers is analyzed, and which independent variables have better prediction power through the multiple regression model is achieved. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) analysis in Multi-Criteria Decision-Making (MCDM) is applied to identify the ranking. The relative distance compared with the best manufacturer and the worst manufacturer is used to evaluate and rank the pros and cons of each ranking. VIKOR is then used to analyze ranking further. The results of data analysis have shown slightly different rankings based on TOPSIS and VIKOR. However, it seems the analysis of VIKOR is more accurate. The comparison of Taiwanese PCB manufacturers before and after the ranking is also revealed. Several practical directions for corporate planning or improvement are suggested. The link between ESG indicators and finance is observed not significant.

The Market Response to the United States COVID-19 in Taiwan’s PCB and Network Communication Industries

This study investigates how COVID-19 in the United States affects the stock prices of the Taiwanese printed circuit board (PCB) and network communication industries. We utilize the event study method to evaluate the market response to the announcements of the United States COVID-19 and the sample period is from February 2020 to April 2020. This study examines whether the COVID-19 caused stock price variations and generated abnormal returns for Taiwanese companies. Finally, this study also evaluates whether there is a significant difference in abnormal returns among companies who have reported corporate social responsibility (CSR) and companies who have not. This study finds that the United States prevention policies have caused a significant influence on abnormal returns in Taiwan’s PCB and network communication industries. Because Taiwan exports PCB to the United States, the stocks price decrease to the United States COVID-19 due to the investors’ expectation of the substantial order decline from the United States in in Taiwan’s PCB and network communication industries. We use ANOVA to prove the difference among the abnormal returns generated by the first, second and third U.S. COVID-19 announcements. The empirical results show that the first announcement caused the largest reactions, and the third announcement caused the least. Furthermore, the United States government’s social distancing policy has caused positive abnormal returns for Taiwan’s PCB and network communication industries due to the great demand for online meeting. Furthermore, we find the significant difference between the implementation and loosening of the social distancing polices. This suggests that investors regard the loosening of the United States social distancing policy signal that the order decline in Taiwan’s PCB and network communication industries. There is no significant difference between the abnormal returns of companies that have reported their CSR practices and that of companies that have not reported.

Application in Self-Adsorption Pd Nanoparticles with High Adhesion

Electroless plating (ELP) is widely used to metallize non-conductive substrates in printed circuit board industry. Due to the complexity of commercial palladium catalysts activation procedure and the high price of palladium, we synthesized a type of palladium catalyst with polyvinyl alcohol (PVA) as protector, formaldehyde (HCHO) as reducing agent. We used polyethylimine (PEI) to modify the surface of the substrate in advance.In this study, we developed a one-step catalyst by adding PEI into PVA-Pd. In addition to its stability, the catalyst has self-adsorption property. The process can be operated at low concentrations, which can effectively reduce the cost. Because of these characteristics, it can not only control the catalyst better than the commercial palladium catalyst but also simplify the process. The effect of self-adsorption was tested on Rogers RO3003 laminates. RO3003 high frequency laminates are ceramic-filled PTFE composites for use in printed circuit boards in commercial microwave and RF applications. The substrate has excellent characteristics such as stable dielectric constant over various temperatures and frequency and low coefficient of thermal expansion. Due to these properties, RO3003 has a great potential to be applied in 5G generation products. After activating, we used electroless copper plating to metallize the substrate.The study focused on the Cu/Pd ratio of the self-adsorption catalyst PEI+PVA-Pd to quantitatively analyze the adsorption capacity by ICP-OES, and the adhesion was analyzed by T-peel. The results indicates that the process is cost-effective, and the adhesion between substrate and copper foil is also higher. The copper layer by using self-adsorption catalyst will also have a better compactness and the surface will be pinhole-free. Therefore, this process can be successfully applied to electroless copper deposition with self-adsorption catalysts.

A Fast Object Detection-Based Framework for Via Modeling on PCB X-Ray CT Images

For successful printed circuit board (PCB) reverse engineering (RE), the resulting device must retain the physical characteristics and functionality of the original. Although the applications of RE are within the discretion of the executing party, establishing a viable, non-destructive framework for analysis is vital for any stakeholder in the PCB industry. A widely regarded approach in PCB RE uses non-destructive x-ray computed tomography (CT) to produce three-dimensional volumes with several slices of data corresponding to multi-layered PCBs. However, the noise sources specific to x-ray CT and variability from designers hampers the thorough acquisition of features necessary for successful RE. This article investigates a deep learning approach as a successor to the current state-of-the-art for detecting vias on PCB x-ray CT images; vias are a key building block of PCB designs. During RE, vias offer an understanding of the PCB’s electrical connections across multiple layers. Our method is an improvement on an earlier iteration which demonstrates significantly faster runtime with quality of results comparable to or better than the current state-of-the-art, unsupervised iterative Hough-based method. Compared with the Hough-based method, the current framework is 4.5 times faster for the discrete image scenario and 24.1 times faster for the volumetric image scenario. The upgrades to the prior deep learning version include faster feature-based detection for real-world usability and adaptive post-processing methods to improve the quality of detections.

A flow cell-based electrochemical system and isotherm-based linear interpolation for the determination of additive concentrations in Cu-Plating baths

Automatic Cu plating bath analyzers are required for the long-term maintenance of the bath quality in the semiconductor and printed circuit board industries. This study reports the development of a flow cell-based electrochemical analyzer for the simple and fast determination of additive concentrations in a Cu plating bath. To determine the additive concentrations, cyclic voltammetry was sequentially performed using a flow cell system for the following solutions: blank, blank + sample, and two references. The stripping charges for each solution were obtained. The concentrations of the additives in the target solution were determined by the linear interpolation of the stripping charge between the two references. To improve the determination accuracy, the stripping charges were reprocessed based on adsorption isotherms. The proposed approach enables the fast and highly accurate determination of organic additives in a Cu electroplating solution.

Integrated Inspection on PCB Manufacturing in Cyber–Physical–Social Systems

The printed circuit boards (PCBs) industry is one of the fastest-growing industries in recent decades. The PCB manufacturing process is highly complicated and severely affected by social factors, which makes it very important to conduct integrated inspection, assuring and improving the production quality. In this article, we propose an artificial systems, computational experiments, and parallel execution-based integrated inspection method in cyber–physical–social systems (CPSS) to realize smart manufacturing. In this inspection system, rather than simply performing modeling, analysis, and control, we perform descriptive intelligence to construct production processes with limited multimodal information, perform predictive intelligence to conduct defect detection and defect prediction, and perform prescriptive intelligence to achieve defect diagnosis and defect management. In this way, our inspection system could offer a learning and training platform for workers to master professional inspection skills, provide an experimentation and evaluation platform for product defect monitoring and early warnings, and supply guidance about defect management and control to improve manufacturing processes. For technical implementation, we leverage a Transformer-based foundation model to achieve knowledge reasoning and human–computer interaction. As a result, we provide an innovative solution to cope with the challenges of quality inspection in current smart manufacturing, and expect its further applications in the PCB industry.

Laser‐Induced Carbonization for Anticounterfeiting Tags

AbstractThe counterfeiting of products is a serious concern for any nation with the increasing activity of counterfeit markets. Anticounterfeiting tags demand low‐cost, unclonable, facile, and ultrafast manufacturing processes. In this study, a laser‐induced carbonization (LIC) technique is employed to produce discrete sizes of LIC spots distributed randomly in an array fashion, as a tag, preferably on a laser wavelength‐sensitive polyimide (PI) film. This technique enables the intrinsic creation of LIC spots in PI without any foreign functional materials, thus avoiding the need for external material. Owing to laser technology, based on the input design and laser processing parameters, the reconfigurable desired output tags can be accomplished in a very short time. Different forms of LIC spots in an optical image are grouped and sorted into three‐level bits based on their sizes and brightness for digitalization. The unique LIC‐based tags can be applied to the flexible printed circuit board industry to address counterfeiting.

An Improved YOLOv5 Network for Detection of Printed Circuit Board Defects

With the rapid development of China’s printed circuit board industry, bare‐board defect detection has high research and application values as an important factor in improving production quality. In this paper, a new detection method based on YOLOv5 is proposed to solve the balance problem of efficiency and performance in the task of circuit board defect detection. First, the k‐means++ method is used to improve the location matching of the prior anchor boxes. Second, the Focal‐EIOU loss function is used instead of GIOU to address the former’s degeneration issue. Finally, the ECA‐Net module is added to enhance the sensitivity of the model to channel features. Experiments were conducted on a public defect dataset, and superior performance was achieved. The proposed method achieves 99.1% mean average precision at 86 frames per second. Compared with other advanced methods, the proposed method achieves the highest mean average precision value, and the detection speed allows real‐time applications.

Detection Algorithm of Surface Defect Word on Printed Circuit Board

For Printed Circuit Board (PCB) surface defect detection, traditional detection methods mostly focus on template matching-based reference method and manual detections, which have the disadvantages of low defect detection efficiency, large errors in defect identification and localization, and low versatility of detection methods. In order to further meet the requirements of high detection accuracy, real-time and interactivity required by the PCB industry in actual production life. In the current work, we improve the You-only-look-once (YOLOv4) defect detection method to train and detect six types of PCB small target defects. Firstly, the original Cross Stage Partial Darknet53 (CSPDarknet53) backbone network is preserved for PCB defect feature information extraction, and secondly, the original multi-layer cascade fusion method is changed to a single-layer feature layer structure to greatly avoid the problem of uneven distribution of priori anchor boxes size in PCB defect detection process. Then, the K-means++ clustering method is used to accurately cluster the anchor boxes to obtain the required size requirements for the defect detection, which further improves the recognition and localization of small PCB defects. Finally, the improved YOLOv4 defect detection model is compared and analyzed on PCB dataset with multi-class algorithms. The experimental results show that the average detection accuracy value of the improved defect detection model reaches 99.34%, which has better detection capability, lower leakage rate and false detection rate for PCB defects in comparison with similar defect detection algorithms.

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.

Biological treatment of high strength monoethanolamine (MEA)-containing wastewater from printed circuit board manufacturing industry

Develop-etch-strip (DES) process in printed circuit board (PCB) manufacturing industries produces wastewater containing high organic nitrogen, in which monoethanolamine (MEA) is the main COD and nitrogen source. This study evaluated feasibility of biological treatment of high concentration MEA-containing acidified DES wastewater collected from a full-scale PCB industry. Batch test results indicated that aerobic conditions attained the highest specific COD degradation rates (6.5–14.6 mgCOD/gVSS/h), anoxic conditions attained second (4.81 mgCOD/gVSS/h), and anaerobic conditions was the lowest (1.3–3 mgCOD/gVSS/h). The anoxic/oxic (A/O) sequencing batch reactor (SBR) with porous polyurethanes carriers, BioNET, was able to treat MEA-containing acidified DES wastewater with 80 % COD removal and 23 % TN removal at influent COD of 300 mg/L, while the A/O continuous stirred tank reactor (CSTR) with BioNET at 8 h of HRT achieved above 96 % COD removal and 78 % TN removal at influent COD of 630 mg/L. The aerobic batch test results indicated that specific COD degradation rates followed a Haldane-type model, suggesting that MEA acts as an inhibitory substrate, especially at concentrations above 2000 mg/L. In addition, MEA inhibited the onset of nitrification, but not specific nitrate production rate, suggesting that MEA may inhibit nitrification by inhibiting expression of amoA gene, based on amoA gene expression results. Finally, the contribution for COD degradation, denitrification and nitrification by BioNET (< 20 %) was lower than that for suspended sludge in the A/O CSTR, suggesting that the role of BioNET carriers could retain microorganisms and served as the habitats for microorganisms to avoid washout at a shorten HRT situation.

Solar water evaporation using porous cellulose polyacrylamide hydrogel with carbon-based material containing copper oxide prepared from after-use adsorbent

The global printed circuit board (PCB) industry produces millions of tons of wastewater containing Cu2+ every year, which exacerbates the shortage of freshwater. As one of low cost, eco-friendly and effective materials, the chitosan-based adsorbent has been widely used for the disposition of PCB industrial sewage. Unfortunately, little attention is paid to the reutilization of waste adsorbent. In this work, we synthesized a carbon-based material containing copper oxide (CMCC) by calcining a chitosan porous adsorption material (CPAM) with Cu2+ as the after-use adsorbent. Then, by loading the CMCC as the photothermal conversion material, a flexible porous cellulose polyacrylamide hydrogel (PCPH) was fabricated, whose light absorbance was 97.4%. Moreover, the PCPH achieved a prominent evaporation rate of 1.80 kg m−2 h−1 under the light intensity of 1.0 kW m−2. In addition, the PCPH possessed superb stability and the desalinated seawater successfully reached the criterion of drinking water from the World Health Organization (WHO). The results revealed that the PCPH with CMCC for solar water evaporation is a possible solution to the reuse of waste adsorbent from PCB industry and the competent remission of freshwater shortage.

Characterization of Additively Produced RF-Structures on Low-Cost PCB Materials Based on Inkjet Technology for Industrial Applications up to 80 GHz

Usually, the printed circuit board industry has to use special subtractive manufacturing methods, if small line and space dimensions down to 50 μm are desired. This drastically raises production effort and costs. In this paper a method is shown, how the inkjet technology can be used to produce passive RF structures with minimum line and space dimensions of 25 μm. For the first time, this is possible over complete panels of low-cost PCB substrates, which have a relatively high surface roughness compared to ceramic or glass carriers. To ensure process reliability, printed lines must also be plated with an additional electroless copper layer to overcome the surface roughness with conductive layers and to lower insertion loss in the two-digit GHz range. Additionally, a surface impedance model is developed, which allows broadband first time right simulations. Finally, additively produced, passive RF filters are compared to their subtractive counterparts to show their benefits regarding production costs and electrical performance up to 80 GHz.

Isothermal Recombinase Polymerase Amplification (RPA) of E. coli gDNA in Commercially Fabricated PCB-Based Microfluidic Platforms.

Printed circuit board (PCB) technology has been recently proposed as a convenient platform for seamlessly integrating electronics and microfluidics in the same substrate, thus facilitating the introduction of integrated and low-cost microfluidic devices to the market, thanks to the inherent upscaling potential of the PCB industry. Herein, a microfluidic chip, encompassing on PCB both a meandering microchannel and microheaters to accommodate recombinase polymerase amplification (RPA), is designed and commercially fabricated for the first time on PCB. The developed microchip is validated for RPA-based amplification of two E. coli target genes compared to a conventional thermocycler. The RPA performance of the PCB microchip was found to be well-comparable to that of a thermocycler yet with a remarkably lower power consumption (0.6 W). This microchip is intended for seamless integration with biosensors in the same PCB substrate for the development of a point-of-care (POC) molecular diagnostics platform.

Improved Mixed-Integer Linear Programming Model for Short-Term Scheduling of the Pressing Process in Multi-Layer Printed Circuit Board Manufacturing

The pressing process is a part of the fabrication process of multi-layer printed circuit board (PCB) manufacturing. This paper presents the application of a new mixed-integer linear programming model to the short-term scheduling of the pressing process. The objective was to minimize the makespan. The proposed model is an improvement from our previous model in the literature. The size complexity of the proposed model is better than that of the previous model, whereby the number of variables, constraints, and the dimensionality of variables in the previous model are reduced. To compare their performance, problems from literature and additional generated test problems were solved. The proposed model was shown to outperform the previous model in terms of computational complexity. It can verify a new optimal solution for some problems. For the problems that could not be solved optimally, the proposed model could find the incumbent solution using much less computational time than the previous model, and the makespan of the incumbent solution from the proposed model was better than or equal to that of the previous model. The proposed model can be a good option to provide an optimal schedule for the pressing process in any PCB industry.

Study on fine lines and undercut suppression of printed circuit board prepared by electrolytic etching

PurposeThe purpose of this paper is to study an electrolytic etching method to prepare fine lines on printed circuit board (PCB). And the influence of organics on the side corrosion protection of PCB fine lines during electrolytic etching is studied in detail.Design/methodology/approachIn this paper, the etching factor of PCB fine lines produced by new method and the traditional method was analyzed by the metallographic microscope. In addition, field emission scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) were used to study the inhibition of undercut of the four organometallic corrosion inhibitors with 2,5-dimercapto-1,3,4-thiadiazole, benzotriazole, l-phenylalanine and l-tryptophan in the electrolytic etching process.FindingsThe SEM results show that corrosion inhibitors can greatly inhibit undercut of PCB fine lines during electrolytic etching process. XPS results indicate that N and S atoms on corrosion inhibitors can form covalent bonds with copper during electrolytic etching process, which can be adsorbed on sidewall of PCB fine lines to form a dense protective film, thereby inhibiting undercut of PCB fine lines. Quantum chemical calculations show that four corrosion inhibitor molecules tend to be parallel to copper surface and adsorb on copper surface in an optimal form. COMSOL Multiphysics simulation revealed that there is a significant difference in the amount of corrosion inhibitor adsorbed on sidewall of the fine line and the etching area.Originality/valueAs a clean production technology, electrolytic etching method has a good development indicator for the production of high-quality fine lines in PCB industry in the future. And it is of great significance in saving resources and reducing environmental pollution.

CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection

In the printed circuit board (PCB) industry, cosmetic defect detection is an essential process to ensure product quality. However, existing PCB cosmetic defect detection approaches have a high false alarm rate, which lead to expensive labor costs of manual confirmation. To solve this problem, some traditional machine learning-based approaches have been proposed, but they just utilize hand-crafted features to build classifiers and thus are rough and sub-optimal. Recently, due to its powerful capability in automatic feature extraction, convolutional neural network (CNN) has been widely used in PCB cosmetic defect detection. However, few of them pay attention to the imbalanced class distribution as well as the different misclassification costs of real and pseudo defects, both of which are common problems in the PCB industry. To this end, in this study, we propose a novel model called cost-sensitive residual convolutional neural network (CS-ResNet) by adding a cost-sensitive adjustment layer in the standard ResNet. Specifically, we assign larger weights to minority real defects based on the class-imbalance degree and then optimize CS-ResNet by minimizing the weighted cross-entropy loss function. We conducted a series of experiments by comparing CS-ResNet with the standard ResNet, state-of-the-art CNN-based approach Auto-VRS and traditional machine learning-based approach HOG-SVM on a real-world PCB cosmetic defect dataset. Experimental results show that CS-ResNet achieves the highest Sensitivity (0.89), G-mean (0.91) and the lowest misclassification costs.

Designing a yield-based skip-lot sampling plan for lot acceptance determination

Acceptance sampling has been widely used in practical applications for incoming or receiving inspections, and various sampling plans have been developed for different circumstances and purposes. Skip-lot sampling plans (SkSPs) are an effective sampling procedure and may be useful as a reduced inspection system when a submitted lot’s quality is good and stable. Thus, this study attempts to integrate the concept of the SkSP and the process yield index Spk to develop a variables SkSP of type 2 (SkSP-2) with an Spk -based single sampling plan (SSP) as the reference plan. This plan is suitable for today’s high-yield process environments and products, with a normally distributed quality characteristic under bilateral specification limits. The plan parameters are solved by an optimisation model with nonlinear constraints, which minimises the average sample number (ASN) and satisfies the quality and risk requirements specified by the producer and consumer. The performance and efficiency of the proposed SkSP-2 are examined and compared with the variables SSP in terms of the operating characteristic and ASN curves. Additionally, tables of the plan parameters under selected conditions are provided for reference, and an example from the printed circuit board industry is presented to demonstrate the practicality of the proposed plan.