Electronic Components Provenance Assurance Through AI and Big Data in Assembly Processes

Abstract— This paper presents a groundbreaking method for real-time authentication of electronic components during the assembly process. Leveraging images captured by pick-and-place machines, our approach utilizes AI and big data algorithms to discern subtle differences in component appearances, allowing for the identification of specific manufacturing lines and authentic manufacturers. By training our algorithm on a vast dataset of over 4 billion components, we achieve exceptional performance parameters in most cases exceeding 99.8%, contingent upon the package case of the component.

Real-life examples illustrate instances of authenticity mismatches encountered in production, highlighting the method’s effectiveness in identifying errors in the supply chain and detecting counterfeit components. Through cross-referencing with top-side images and supply chain documentation, we validate the results, establishing a new standard for authenticity assurance without reliance on traditional methods. This methodology represents a true AI revolution, promising enhanced supply chain integrity, cost reduction, and mitigation of risks associated with counterfeit components.

Index Terms— Authentication, Electronic Components, Counterfeit Detection, Artificial Intelligence, Big Data Analytics, Manufacturing, Supply Chain Management, Provenance, Traceability, Pick-and-Place Machines, Machine Learning, Image Analysis.

I. INTRODUCTION

The rise of counterfeit electronic components poses significant challenges to manufacturers, distributors, and consumers alike. Counterfeit components not only jeopardize the integrity and performance of end products but also pose serious safety risks, particularly in critical applications such as aerospace, automotive, and medical devices. In-line production, characterized by the continuous assembly and testing of electronic products within manufacturing facilities, presents a unique set of challenges for ensuring component authenticity. Traditional methods of component authentication, such as manual inspection [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], are often inadequate in detecting sophisticated counterfeit schemes prevalent in today’s global supply chains. Consequently, there is a pressing need for advanced and automated solutions capable of identifying counterfeit components in real-time during the production process. The significance of electronic component authentication and counterfeit detection in in-line production extends beyond ensuring product quality and safety. It also encompasses broader concerns related to supply chain integrity, intellectual property protection, and regulatory compliance. Manufacturers face increasing pressure to implement robust authentication measures to safeguard their brand reputation and mitigate potential legal liabilities associated with the use of counterfeit components.

A. Overview of Existing Challenges in Ensuring Component  Authenticity

Despite advancements in manufacturing and supply chain  management, ensuring the authenticity of electronic  components remains a formidable challenge for industry  stakeholders. The globalization of manufacturing has led to  increasingly complex and fragmented supply chains, spanning  multiple countries and involving numerous suppliers and  distributors. This complexity creates opportunities for  counterfeiters to introduce fake components into the supply  chain, often exploiting vulnerabilities in sourcing practices  and distribution networks. Counterfeiters continually evolve  their techniques to mimic genuine electronic components,  making detection increasingly difficult. These techniques may  include re-marking, re-packaging, and even manufacturing  counterfeit components that closely resemble authentic ones [9], [10], [11], [12], [13]. As a result, traditional methods  based solely on examination or manual testing are no longer sufficient to identify counterfeit components reliably and in scale.

To the untrained eye, components from different manufacturers may appear identical when housed in the same package case. This similarity is expected, given that they serve the same function and should occupy the same footprint on the PCB. However, upon closer examination of images depicting components from various manufacturers, subtle yet consistent
differences may be identified. Fig. 1, Fig. 2, and Fig. 3 showcase examples of images from six different manufacturers of MLCC, SOT-23-3, and QFN-16 components, respectively. These nuanced distinctions become evident upon scrutiny.

Fig. 1. Example images of MLCC’s made by 6 different manufacturers. The names of the manufacturers are presented below each image.

Fig. 2. Example images of Small Outline Transistor packages (SOT-23-3) made by 6 different manufacturers. The names of the manufacturers are presented below each image.

Fig. 3. Example images of IC’s in package case QFN-16 made by 6 different manufacturers. The names of the manufacturers are presented below each image.

Limited transparency and traceability within supply chains hinder efforts to verify the authenticity of electronic components[14], [15], [16], [17], [18]. In many cases, manufacturers lack comprehensive visibility into the origins and movements of components throughout the supply chain, making it challenging to validate their authenticity accurately. Furthermore, the absence of standardized traceability practices across the industry exacerbates these challenges, resulting in inefficiencies and inconsistencies in authentication processes. Traditional authentication methods, such as destructive testing or sample-based forensics, are often costly and time consuming, especially in production environments. Manufacturers face pressure to balance the need for rigorous authentication measures with the imperative to maintain efficient production workflows and meet demanding production schedules. As a result, there is a growing demand for non-invasive and real-time authentication solutions capable of integrating seamlessly into existing production processes [19], [20], [21], [22]. Compliance with regulatory requirements, such as RoHS (Restriction of Hazardous Substances) and REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals), adds another layer of complexity to electronic component authentication. Non-compliance with these regulations not only exposes manufacturers to legal liabilities but also undermines consumer confidence in product safety and environmental sustainability.

B. Introduction to the Proposed Method Leveraging AI and Big Data Approaches

To address the challenges and advance the state-of-the-art in electronic component authentication, this paper introduces a novel method that harnesses the power of artificial intelligence (AI) and big data analytics[19], [23]. Our approach is designed to provide manufacturers with a robust and scalable solution for detecting counterfeit components and ensuring the integrity of in-line production processes. At the heart of our proposed method is the utilization of existing cameras installed in pick and place machines within production lines [24]. These cameras capture bottom-side images of assembled components on printed circuit boards (PCBs) as they are mounted during the manufacturing process. Leveraging AI algorithms trained on large datasets of authentic components, our method intelligently analyzes these images to identify and classify manufacturers with a high degree of accuracy[23]. Furthermore, our method incorporates big data techniques to compare the manufacturer assignments obtained from image analysis with documented traceability information stored in centralized databases [14], [15], [25]. By cross-referencing the identified manufacturers with expected suppliers and component origins, our approach enables real-time detection of mismatches that may indicate counterfeit components or errors in the supply chain. The integration of AI and big data approaches offers several key advantages over traditional authentication methods. Firstly, it enables non-invasive and real-time authentication of components during the production process, minimizing disruptions to workflow efficiency. Secondly, the method can adapt and evolve to detect emerging counterfeit trends and sophisticated counterfeit techniques. Moreover, the scalability and versatility of AI and big data technologies make the method applicable across a wide range of production environments and electronic component types.

C. Challenges in Handling a Vast Array of Component Types

One of the primary challenges in developing an effective electronic component authentication system for in-line production lies in the diversity and volume of components present in the market. With hundreds of millions of component types manufactured by thousands of different companies across various locations worldwide, creating a model capable of accurately identifying and classifying such a vast array of classes presents a formidable obstacle. The complexity of the challenge becomes evident when considering the diverse range of factors that contribute to the uniqueness of each component type. Variations in size, shape, markings, and packaging, coupled with subtle differences in manufacturing processes and materials, further exacerbate the difficulty of developing a universal authentication model. Moreover, the dynamic nature of the electronics industry, characterized by rapid innovation and product evolution, necessitates constant updates and adaptations to keep pace with emerging component types and counterfeit techniques.

In addition to the breadth of component types, the demand for high accuracy poses another significant challenge in this endeavor. In in-line production environments, where production processes operate at high speeds and efficiency is paramount, even a small error in component authentication can have substantial consequences. A false identification could potentially disrupt production flow, leading to delays, rework, and increased costs. Furthermore, the cost implications of errors in authentication cannot be overstated. In addition to direct financial losses resulting from production downtime and component replacement, there are broader ramifications for brand reputation, customer trust, and regulatory compliance.

While advancements in AI and big data offer promising solutions [21], [22], [26], overcoming the inherent complexities of electronic component authentication in in-line production will require ongoing research, experimentation, and refinement.

II. LITERATURE REVIEW

Electronic component authentication and counterfeit detection pose persistent challenges in manufacturing and supply chain management[9], [10], [11], [13], [19], [20], [21]. Various methods and techniques have evolved over time to tackle these issues, ranging from External Visual Inspection (EVI), packaging configuration and dimensions, scanning electron microscopy inspection, remarking and resurfacing, resistance to solvents, scrape, dynasolve as per the growing SAE AS6171/2 standard [27]. Visual inspection, historically a primary method, involves examining physical characteristics like markings and packaging, but it struggles with sophisticated counterfeits. Destructive testing offers detailed insights into material composition but is impractical for large scale production. Chemical analysis and electrical testing provide non-destructive options, albeit with limitations [27]. RFID technology and blockchain offer potential solutions but face implementation challenges. Collaborative platforms facilitate information exchange to combat counterfeits.

AI has transformed electronic manufacturing by optimizing processes and ensuring compliance. Predictive analytics enable demand forecasting and inventory management, while AI-powered quality control systems detect defects in real time. Predictive maintenance systems prevent equipment failures, minimizing downtime. In visual inspection for defects, deep learning algorithms enhance accuracy by recognizing patterns indicative of anomalies [23], [28], [29], [30], [31], [32], [33], [34], [35]. These AI-based systems offer scalability and consistency, transforming quality control in manufacturing.

Our method for detecting counterfeits, defects, solderability issues, and corrosion was published in [35], [36], [37]. By applying this method to mass materials, we attained a unique position to evaluate the state of components from a statistical perspective. Consequently, we published work on the occurrence of corrosion and solderability issues in passive components [35], [37]. Additionally, we documented several use cases of corrosion detected using this method, subsequently verified by lab analysis [32], [33], [34]. Moreover, we devised a method to mitigate the risk of cracks in MLCCs (Multilayer Ceramic Capacitors) by early detection of evidence of corrosion in the soldering terminals, as we demonstrated that corrosion serves as a precursor for crack development [31].

To address the imperative need for real-time reaction and to prevent the mounting of defective components, it became crucial to shift the processing to the edge near the mounting machines. The round trip of the image to the cloud would exceed the available time frame between pickup and placement, which is measured in milliseconds. Therefore, in [38] a method for performing real-time processing at the edge in a manner fast enough to react instantaneously and prevent the mounting of defective components within milliseconds is presented.

III. METHODOLOGY

The process initiates with acquiring images of assembled components on printed circuit boards (PCBs) using cameras installed in pick and place machines within the production line [24]. These cameras capture bottom-side images of components during the mounting process, serving as input data for subsequent analysis.

Prior to analysis, the acquired images undergo preprocessing to enhance quality and remove noise. Preprocessing techniques may include noise reduction, contrast enhancement, and image normalization to ensure consistency and accuracy in subsequent processing steps.

Once preprocessed, the images are analyzed to extract relevant features and characteristics indicative of component authenticity. The features may identify unique markings, indentations, metallization shape, leads shape, ball array, surface roughness, graininess, or other distinguishing attributes specific to each manufacturer. The features extraction is done by a convolutional neural networks, trained on large datasets of components from many different sources. The AI model analyzes the extracted features to classify components into different categories based on their manufacturer specific fingerprint.

To address the challenge posed by the vast number of component types, the method utilizes a patented [23] approach that analyzes smaller groups with more prior knowledge specific to the components’ manufacturing and composition.

We use the fact that manufacturers typically use the same packaging machine and processes to package many components of the same package case. For example, the standard SOT-23 package case may host thousands of different functionalities and variations, yet they are all packaged by the same machine regardless of the functionality and the die inside it. Therefore, finding the fingerprint of the machine that packaged it imprinted is key to further reducing the number of classes without affecting accuracy. By classifying the package case used, we narrow down the variants, thus enhancing performance and accuracy.

We employ an unsupervised approach to cluster the visual features of components into groups of the same package case. These homogeneous visual feature groups are then correlated with the identified package case and all its synonyms. This process allows us to establish a common ground for package case names and enables accurate classification of components based on their visual features.

AI algorithms for manufacturer assignment and mismatch detection are optimized for efficiency and accuracy. Cluster analysis techniques verify consistency in labeling, essential for training accurate models. Real-time decision-making facilitates proactive measures, such as blocking counterfeit reels during production.

Subsequently, the feature map is flattened and fed into a fully connected neural network layer, which further processes the extracted features to make predictions regarding the component’s manufacturer. The neural network parameters are optimized during training using a labeled dataset of authentic components, enabling the model to learn discriminative representations for different manufacturers.

Integration of big data techniques enhances traceability information comparison. Verification of authenticity through top marking inspection allows differentiation between errors and potential counterfeits. Discrepancies prompt further investigation to confirm component authenticity and trace origins.

A. Algorithm Description

The algorithm flowchart is presented in Fig. 4. The algorithm’s core functionality revolves around the classification of manufacturers using deep learning methodologies within the realm of computer vision.

Insufficient data can severely impair the model’s ability to generalize effectively, potentially leading to overfitting and suboptimal performance on unseen data. Therefore, ensuring an ample volume of high-quality training data is paramount to the success of the method.

To mitigate the risk of overfitting and enhance the model’s capacity to discern subtle patterns and variations in images, we employ a rigorous approach to dataset selection. Specifically, we only initiate the training process for manufacturers for which we have amassed a sufficient volume of data. This entails ensuring that the number of images within the training set surpasses a predefined threshold, thereby ensuring the model’s robustness and efficacy.

Fig. 4. Image processing involves two parallel blocks: one for the original image and another for its edge representation. The final output combines the results from both blocks, each designed similarly but with slight variations in filter usage.

Fig. 5. Algorithm for manufacturer identification. The combination of logit scores and manufacturer classification allows the classifiers to assess the similarity of unknown manufacturers to known ones based on their respective scores.

Central to the authentication process is the combination of logit scores and manufacturer classification. Logit scores play a role in discerning unknown manufacturers, serving as indicators of confidence levels associated with each manufacturer. By analyzing the logits, classifiers can assess the similarity of unknown manufacturers to known ones based on their respective scores (see Fig.5).

One of the fundamental components of our algorithm is the feature model, which diverges from conventional classification models through its parallel architecture (Fig. 4). Upon receiving an input image, the feature model processes it through two parallel blocks: one dedicated to the original image and another to its edge representation. The final output is a fusion of the outputs from these two blocks, leveraging the strengths of both approaches to enhance classification accuracy.

Our base model lays the foundation for this feature model, with a design that emphasizes parallel processing and efficient information fusion. While both blocks share a similar design, slight variations exist in the configuration of convolutional layers and the number of filters utilized, allowing for flexibility and adaptability across different datasets and scenarios.

A. Visualization of Dominant Features with Heat Maps

Understanding where a neural network focuses its attention can provide valuable insights into its decision-making process. One method for visualizing this focus is Gradient-weighted Class Activation Mapping, or Grad-CAM. Grad-CAM operates by leveraging the gradients of a target concept, which flow into the final convolutional layer of the network. By analyzing these gradients, Grad-CAM generates a coarse localization map, referred to as a heat map. This heat map highlights the important regions in the input image that contribute to the network’s prediction of the target concept.

However, in our specific network architecture, the final convolutional layer is positioned distantly from the output layer. Moreover, this layer is associated with parallel branches of the network, further complicating the application of Grad CAM. As a result, utilizing Grad-CAM for meaningful localization in our network proves challenging and may not yield direct results but point on the leading features. Fig. 7 presents some examples of heat maps of the components that are also presented in table X.

In the presented examples, distinctive visual features play a role in component classification. For the resistors and MLCCs case, the texture of the leads emerges as a dominant feature. In the tantalum capacitors case, it’s the rounded edges of the lead plates that stand out. Conversely, in QFN components, attention is drawn to the shape of the heat plate, while in SOD
323 packages, the defining feature lies in the trapezoidal shape of the edges. It’s important to note that while these features are significant for the highlighted examples, they may not universally apply across all classifications.

Fig. 6. Feature heat maps of different cases highlighting the dominant features that resulted in the classification.

IV. EXPERIMENTAL SETUP

Our experiments are conducted in a controlled manufacturing environment equipped with modern production lines and inspection systems. These production lines feature state-of-the-art pick and place machines from industry-leading manufacturers, including ASMPT and Fuji, recognized for their precision and reliability in electronic component assembly.

Operating under real-world manufacturing scenarios, the production lines facilitate continuous assembly and testing processes. Multiple production lines, each equipped with pick and place machines, are utilized to generate diverse datasets reflecting variations in manufacturing conditions and component types.

To evaluate the performance of electronic component authentication algorithm, we employ two primary methods:

Classical machine learning techniques are utilized to construct confusion matrices based on test datasets. We select representative package cases and evaluate metrics such as accuracy, precision, and recall assessing the overall effectiveness of our authentication method.

In addition, we compare top-view images captured by automated optical inspection (AOI) machines further along the line with bottom-view predictions generated by the authentication algorithm. This visual comparison enables us to identify fits and mismatches between expected and observed manufacturers, providing insights into the accuracy and reliability of our authentication method.

Our experimental dataset comprises data collected from over 20 different production lines utilizing ASMPT and Fuji pick and place machines. This extensive dataset includes over 10 million components, representing various component types, manufacturers, and production conditions, ensuring robust evaluation and validation of our authentication method under diverse real-world scenarios.

The dataset employed for training and validation comprises over 4 billion labeled images of components captured from the bottom side by pick and place machines and over 100 million components imaged from the top side using automated optical inspection (AOI) systems.

Data acquisition was facilitated through a custom application programming interface (API) designed to interface with pick and place machines and AOI systems seamlessly [24]. This API enabled the extraction of images without disrupting production operations, ensuring continuous data collection in real-time.

A rigorous verification process, as outlined in our methodology, was implemented to establish the authenticity and traceability of components within the dataset. This process involved cross-referencing traceability information obtained from production logs with manufacturers assigned by our authentication algorithm, ensuring dataset accuracy and reliability.

The non-intrusive nature of the data collection process minimized disruption to production operations, with the API interfacing with machines in full producing mode. Continuous data acquisition was achieved while maintaining optimal manufacturing efficiency.

IV. RESULTS

A. AI Model Performance Parameters

In this section, we present the results of our experiments, focusing on the machine learning training and testing parameters for sample passive components and integrated circuits (ICs). The objective is to demonstrate the effectiveness of the proposed method in authenticating electronic components in in-line production lines.

For the ML training and testing phase, we selected sample passive components and ICs to represent a diverse range of component types commonly encountered in electronic manufacturing. It is worth noting that the number of features varies depending on the size of the component, with smaller components typically exhibiting fewer distinctive features.

Superficially, it may appear that all multilayer ceramic capacitors (MLCCs) are indistinguishable to the naked eye (see Fig. 1). However, close analysis reveals subtle differences in surface texture, roughness, roundness, and other visual attributes that allow for differentiation. Fig. 7 illustrates these differences, highlighting the distinct characteristics that enable ML algorithms to classify MLCCs accurately.

Similarly, the analysis of integrated circuits demonstrates that components housed in the same packages but manufactured by different companies exhibit discernible differences. Fig. 2 and Fig. 3 showcases examples of IC packages from various manufacturers, showcasing the variations in surface features and markings. These differences, although subtle, are crucial for accurate authentication and counterfeit detection.

To assess the accuracy of the authentication method, we employ quantitative evaluation metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into the performance of our ML models in correctly classifying components across different package cases and manufacturers.

We construct confusion matrices based on test datasets to visualize the classification results and evaluate the performance of our authentication method (example for MLCC 1210 is presented at Fig. 7. By analyzing the confusion matrices, we can identify any misclassifications or false positives/negatives and refine our ML models accordingly.

Fig. 7. Confusion matrix for a small batch of MLCC 1210 classification results.

The performance parameter for this case is exceeding 99.3%. The summary of the performance parameters for different component types is presented in Table I.

Furthermore, we compare the performance of our proposed method with traditional authentication methods such as visual inspection, destructive testing, and manual verification. This comparison highlights the advantages of our AI-driven approach in terms of accuracy, efficiency, and scalability. The confidence level of a lab operating SAE AS 6171A/B is approximately 90% [27]. The performance of the presented algorithms exceeds 99%.

B. Real-world Case Studies

In this section, we present visual examples derived from actual production line data. Our method has been rigorously applied to real-world scenarios, and the discrepancies between the expected and analyzed data serve as clear indicators of mismatched cases. In one example, the system detected the mounting of an obsolete component, the MAX3232ID, which is a 5V RS-232 transceiver IC from Texas Instruments. This component, no longer in production, was mistakenly mounted instead of the required ADM3202ARNZ, a 3V RS-232 transceiver IC from Analog Devices. The detection occurred through analysis of the bottom view image (depicted on the left side of Fig. 8), which identified the component as originating from Texas Instruments. This determination was subsequently confirmed through visual inspection of the top view image, which displayed the TI marking (depicted on the right side of Fig. 8).

The algorithm identified Texas Instruments as the manufacturer during bottom view analysis, contrary to the documented manufacturer indicated through the traceability system, which was Analog Devices. This discrepancy doesn’t necessarily imply fraudulent activity. It could stem from errors in traceability logging or supply chain issues. These instances highlight the vital role of automated inspection systems in identifying discrepancies and maintaining component integrity and provenance.

In another scenario, the production system documented the component as QS3VH245QG, identified as Renesas Electronics Digital Bus Switch ICs. However, upon analyzing the bottom view of the component (depicted on the left side of Fig. 9), the algorithm detected the manufacturer as Texas Instruments, a determination later confirmed by the top view image obtained through AOI (shown on the right side of Fig. 9). Specifically, the detected part was SN74CBTLV3245ADQBR from Texas Instruments, classified as a 5-Channel, 3.3-V Bus Switch with Automotive Grade 1 specifications.

Fig. 8. Detecting Obsolete Component Substitution. The figure shows detection of an obsolete MAX3232ID from Texas Instruments instead of the required ADM3202ARNZ from Analog Devices. The left side displays the bottom view analysis, identifying the component as Texas Instruments, confirmed by the TI marking in the top view (right side).

Fig. 9. Left: Bottom view image analysis detected the manufacturer as Texas Instruments, while the traceability system documented the component as QS3VH245QG from Renesas Electronics. Right: Top view image obtained through AOI confirmed the detected component as SN74CBTLV3245ADQBR from Texas Instruments, an interchangeable 5-Channel, 3.3-V Bus Switch with Automotive Grade 1 specifications.

Despite both components being considered interchangeable according to the AVL, the traceability information was incorrect. Consequently, in the event of a malfunction, the traceability system would erroneously advise recalling the wrong parts. While cases like these could be resolved through top-view automated marking verification (as presented in [25]), many components lack such markings. Therefore, the provenance of the component can be verified solely by the algorithm and confirmed through scrutiny of the supply chain documents.

An instance of this analysis is evident in the following case study. Initially documented as Panasonic, a passive component was identified as Vishay solely through bottom view analysis. For visual comparison, the image can be compared with authenticated images of Vishay (depicted in Fig. 10, center) and Panasonic (shown in Fig. 10, right). Each recognized component is meticulously recorded with the corresponding PCB serial number on which it was mounted, alongside its specific reference designation, facilitating reworking if necessary.

In the following example (Fig. 11, top right), a component was reported as Bourns; however, upon inspection, the component image did not match the model of any Bourns manufacturer in the AVL or any known manufacturer (top left). The PCB serials and reference designations of the mounted components are presented at the bottom of Fig. 11.

V. DISCUSSION

Interpreting the results of the study in the context of electronic component authentication and counterfeit detection sheds light on the effectiveness and implications of our proposed methodology. The high accuracy rate achieved by our authentication method, averaging over 99.8% with no signs of overfitting, underscores its potential to address the pressing challenges posed by counterfeit electronic components in in-line production. Furthermore, the observed differences in visual features among seemingly identical components, such as MLCCs and ICs, highlight the nuanced characteristics that serve as distinguishing markers for authentication. These subtle variations, captured by the neural network model, enable reliable classification and detection of counterfeit components, enhancing the integrity and safety of electronic products.

Fig. 10. Comparison of bottom view images of a passive component initially documented as Panasonic (center) and subsequently identified as Vishay (top right). The verified image of Vishay (top left) provides a basis for comparison, highlighting the subtle differences that led to the accurate identification of the component.

Fig. 11. Comparison of reported Bourns component image (top right) with manufacturer model (top left) and presentation of mounted component PCB serials and reference designations (bottom).

The implications of the study extend beyond the realm of electronic component authentication, with far-reaching benefits for manufacturing processes and supply chain management. By integrating AI-driven authentication systems into in-line production lines, manufacturers stand to gain significant improvements in quality control, operational efficiency, and supply chain resilience. The real-time detection of counterfeit components enhances product integrity and safety, mitigating the risk of faulty products reaching consumers. By establishing standardized authentication practices and data-sharing protocols, manufacturers, distributors, and regulatory agencies can collectively combat counterfeit threats and uphold industry standards. This collaborative approach strengthens supply chain integrity, enhances consumer confidence, and fosters a culture of trust and accountability.

VI. CONCLUSION

The article introduces a pioneering method for real-time authentication of electronic components during the assembly process. Leveraging images captured by pick-and-place machines for alignment, the approach harnesses AI and big data algorithms to discern subtle differences in component appearances, indicative of the machine that packed them. By training the algorithm on a vast dataset of over 4 billion components, we establish a fingerprint for each of the components production line’s specific features, extending to authenticate the manufacturer. Through extensive testing on a large dataset, our Convolutional Neural Network achieves performance parameters averaging over 99.8%, contingent upon the package case of the component.

Real-life examples are presented to explain instances of authentic mismatches encountered in production. These discrepancies can be attributed to supply chain errors, inaccuracies in traceability processes, and, in some cases, fraudulent counterfeit components. To validate the results, we cross-reference them with top-side images containing component traceability data and supply chain documentation.

This methodology marks a paradigm shift, as it enables the authentication of all components without relying on cumbersome, costly, and often unreliable supply chain documents or sample testing. By ensuring the provenance of all components used in production, the method establishes a new standard for authenticity assurance. Moreover, the same algorithmic concept is extended to authentication using top view images obtained from AOI machines, offering a comprehensive solution for component authentication.

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[27]M. H. Azarian, “An Overview of Risk-Based EEE Counterfeit Part  Detection Based on SAE AS6171,” in ISTFA 2018: Proceedings from the  44th International Symposium for Testing and Failure Analysis, ASM  International, 2018. 

[28]Z. Jokovic, G. Jankovic, S. Jankovic, A. Supurovic, and V. Majstorović,  “Quality 4.0 in Digital Manufacturing–Example of Good Practice,”  Quality Innovation Prosperity, vol. 27, no. 2, pp. 177–207, 2023. 

[29]K. S. S. Liyakat and K. K. S. Liyakat, “ML in the Electronics  Manufacturing Industry,” Journal of Switching Hub, vol. 8, no. 3, pp. 9– 13, 2023. 

[30]M. Javaid, A. Haleem, R. P. Singh, and R. Suman, “Significance of  Quality 4.0 towards comprehensive enhancement in manufacturing  sector,” Sensors International, vol. 2, p. 100109, 2021. 

[31]E. Weiss, “Detecting Corrosion to Prevent Cracks in MLCCs with AI,”  Journal of Failure Analysis and Prevention, 2023, doi: 10.1007/s11668- 023-01824-3. 

[32]E. Weiss, “Revealing Hidden Defects in Electronic Components with an  AI-Based Inspection Method: A Corrosion Case Study,” IEEE Trans  Compon Packaging Manuf Technol, 2023, doi:  10.1109/TCPMT.2023.3293005. 

[33]E. Weiss, “Preventing Corrosion-related Failures in Electronic Assembly:  A Multi-case Study Analysis,” IEEE Trans Compon Packaging Manuf  Technol, May 2023, doi: 10.1109/TCPMT.2023.3285776. 

[34]E. Weiss, “AI Detection of Body Defects and Corrosion on Leads in  Electronic Components, and a study of their Occurrence,” in 2022 IEEE  International Symposium on the Physical and Failure Analysis of  Integrated Circuits (IPFA), IEEE, 2022, pp. 1–6. 

[35]E. Weiss, “Electronic component solderability assessment algorithm by  deep external visual inspection,” in 2020 IEEE Physical Assurance and  Inspection of Electronics (PAINE), IEEE, 2020, pp. 1–6. 

[36]E. Weiss, “Electronic Component Analytics and Traceability.” [Online].  Available: www.cybord.ai 

[37]E. Weiss and Z. Efrat, “System and method for nondestructive assessing  of solderability of electronic components,” P-603537-PC, 2021 [38]E. Weiss, S. Caplan, K. Horn, and M. Sharabi, “Real-Time Defect  Detection in Electronic Components during Assembly through Deep  Learning,” Electronics (Switzerland), vol. 13, no. 8, Apr. 2024, doi:  10.3390/electronics13081551. 

Eyal Weiss is a multidisciplinary technology expert, with a background in machine learning, plasma physics, optical assemblies, laser technology, and electromagnetics. He is a recipient of multiple awards, including the prestigious “Israel security prize” twice. He has 17 years of experience leading research at Soreq Research Center and has built manufacturing production lines utilizing advanced technology and AI. He holds a B.Sc in Mechanical  Engineering, M.Sc. in Plasma Physics from the Technion-Israel  Institute of Technology, and a Ph.D. in Electronic and Computer  Engineering. He is the founder and CTO of Cybord, which  develops electronic component qualification and authentication  technologies. Dr. Weiss is a member of the Israel Innovation  Authority, EIC, SAE, and IPC committees and has published over  28 peer-review articles, 6 patents, and a book.