A Review on Automatic Bill of Material Generation and Visual Inspection on PCBs

S, Mukhil Azhagan M.; Mehta, Dhwani; Lu, Hangwei; Agrawal, Sudarshan; Tehranipoor, Mohammad; Asadizanjani, Navid; Chawla, P.

doi:10.31399/asm.cp.istfa2019p0256

Cited by 20 publications

(7 citation statements)

References 0 publications

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“…Generally, unsophisticated counterfeits can be spotted by discrepancies between genuine and suspect systems (e.g., [25,26]). A few whole-PCBA automated inspection projects are under development [27] but are immature. X-ray analysis of bare PCBs has been proposed but cannot be applied to assemblies because mounted components cause x-ray artifacts [28].…”

Section: 21mentioning

confidence: 99%

FPIC: A Novel Semantic Dataset for Optical PCB Assurance

Jessurun¹,

Dizon-Paradis²,

Harrison³

et al. 2022

Preprint

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The continued outsourcing of printed circuit board (PCB) fabrication to overseas venues necessitates increased hardware assurance capabilities. Toward this end, several automated optical inspection (AOI) techniques have been proposed in the past exploring various aspects of PCB images acquired using digital cameras. In this work, we review state-of-the-art AOI techniques and observed the strong, rapid trend toward machine learning (ML) solutions. These require significant amounts of labeled ground truth data, which is lacking in the publicly available PCB data space. We propose the FICS PBC Image Collection (FPIC) dataset to address this bottleneck in available large-volume, diverse, semantic annotations. Additionally, this work covers the potential increase in hardware security capabilities and observed methodological distinctions highlighted during data collection.

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Section: 21mentioning

confidence: 99%

FPIC: A Novel Semantic Dataset for Optical PCB Assurance

Jessurun¹,

Dizon-Paradis²,

Harrison³

et al. 2022

Preprint

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“…All these methods address a specific problem in PCB analysis but do not propose a solution to address the PCB security, validation and assurance. Therefore, a novel segmentation method is required for applications such as Automatic Bill of Material (AutoBoM) [5] to automatically…”

Section: Related Workmentioning

confidence: 99%

“…A significant focus of the research studies has been performed for automatic defect detection of the PCBs, but very limited work is available on automatic component detection and identification of PCBs. Automatic Bill of Materials Extraction (AutoBoM) is one such approach which uses a Bill of Material (BoM) file as an input to the system to validate and authenticate the PCBs [5] in cross comparison with the Golden BoM from the manufacturers. Our work can be employed to aid both human operators by directly replacing and improving current physical inspection, but also aid in developing a fully automated detection solution (As a region proposal stage for Neural Networks).…”

Section: Introductionmentioning

confidence: 99%

EC-Seg: Electronic Component Segmentation for PCB Assurance and Counterfeit Avoidance

Sathiaseelan

Agrawal

Vutukuru

et al. 2021

International Symposium for Testing and Failure Analysis

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PCB Assurance currently relies on manual physical inspection, which is time consuming, expensive and prone to error. In this study, we propose a novel automated segmentation algorithm to detect and isolate PCB components from the boards called EC-Seg. Segmentation and component localization is a vital preprocessing step in component identification, component authentication, as well as in detecting logos and text markings in components. EC-Seg is an efficient method to automate Quality assurance tool-chains and also to aid Bill of Material Extraction in PCBs. Finally, EC-Seg can be used as a Region proposal algorithm for object detection networks to detect and classify microelectronic components, and also to perform sensor fusion with X-Rays to aid in artifact removal in PCB X-Ray tomography. Index Terms—PCB Hardware Assurance, Component Segmentation, Component detection, AutoBoM, Physical Inspection, Visual inspection, Counterfeit detection

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“…BoMs have several uses outside the area of hardware assurance as well, such as reverse engineering (e.g., foreign, competitor, or legacy device analysis), industrial assessments (e.g., cost estimation, quality assurance), and academic research (e.g., technology trend analysis) [8]. Toward this end, we developed AutoBoM, which is a framework for automatically extracting a BoM using optical images of a target PCB [9].…”

Section: Introductionmentioning

confidence: 99%

Why Is Deep Learning Challenging for Printed Circuit Board (PCB) Component Recognition and How Can We Address It?

et al. 2021

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In this paper, we present the need for specialized artificial intelligence (AI) for counterfeit and defect detection of PCB components. Popular computer vision object detection techniques are not sufficient for such dense, low inter-class/high intra-class variation, and limited-data hardware assurance scenarios in which accuracy is paramount. Hence, we explored the limitations of existing object detection methodologies, such as region based convolutional neural networks (RCNNs) and single shot detectors (SSDs), and compared them with our proposed method, the electronic component localization and detection network (ECLAD-Net). The results indicate that, of the compared methods, ECLAD-Net demonstrated the highest performance, with a precision of 87.2% and a recall of 98.9%. Though ECLAD-Net demonstrated decent performance, there is still much progress and collaboration needed from the hardware assurance, computer vision, and deep learning communities for automated, accurate, and scalable PCB assurance.

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A Review on Automatic Bill of Material Generation and Visual Inspection on PCBs

Cited by 20 publications

References 0 publications

FPIC: A Novel Semantic Dataset for Optical PCB Assurance

FPIC: A Novel Semantic Dataset for Optical PCB Assurance

EC-Seg: Electronic Component Segmentation for PCB Assurance and Counterfeit Avoidance

Why Is Deep Learning Challenging for Printed Circuit Board (PCB) Component Recognition and How Can We Address It?

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