Please use this identifier to cite or link to this item:
Title: Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach
Authors: Starodubov, D
Danishvar, S
Abu Ebayyeh, AARM
Mousavi, A
Keywords: PCB components recognition;capsule networks;convolutional neural networks;data fusion;WaferCaps;image classification
Issue Date: 10-May-2024
Publisher: MDPI
Citation: Starodubov, D. et al. (2024) 'Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach', Electronics, 13 (10), 1863, pp. 1 - 18. doi: 10.3390/electronics13101863.
Abstract: Microelectronics and electronic products are integral to our increasingly connected world, facing constant challenges in terms of quality, security, and provenance. As technology advances and becomes more complex, the demand for automated solutions to verify the quality and origin of components assembled on printed circuit boards (PCBs) is skyrocketing. This paper proposes an innovative approach to detecting and classifying microelectronic components with impressive accuracy and reliability, paving the way for a more efficient and safer electronics industry. Our approach introduces significant advancements by integrating optical and X-ray imaging, overcoming the limitations of traditional methods that rely on a single imaging modality. This method uses a novel data fusion technique that enhances feature visibility and detectability across various component types, crucial for densely packed PCBs. By leveraging the WaferCaps capsule network, our system improves spatial hierarchy and dynamic routing capabilities, leading to robust and accurate classifications. We employ decision-level fusion across multiple classifiers trained on different representations—optical, X-ray, and fused images—enhancing accuracy by synergistically combining their predictive strengths. This comprehensive method directly addresses challenges surrounding concurrency, reliability, availability, and resolution in component identification. Through extensive experiments, we demonstrate that our approach not only significantly improves classification metrics but also enhances the learning and identification processes of PCB components, achieving a remarkable total accuracy of 95.2%. Our findings offer a substantial contribution to the ongoing development of reliable and accurate automatic inspection solutions in the electronics manufacturing sector.
Description: Data Availability Statement: Data supporting the findings of this study are unavailable due to privacy restrictions.
ISSN: 1450-5843
Other Identifiers: ORCiD: Sebelan Danishvar
ORCiD: Abd Al Rahman M. Abu Ebayyeh
ORCID Alireza Mousavi
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfCopyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons