Affiliation:
1. School of Engineering, University of Connecticut, Storrs, CT 06269, USA
Abstract
This paper presents an innovative solution to the challenge of part obsolescence in microelectronics, focusing on the semantic segmentation of PCB X-ray images using deep learning. Addressing the scarcity of annotated datasets, we developed a novel method to synthesize X-ray images of PCBs, employing virtual images with predefined geometries and inherent labeling to eliminate the need for manual annotation. Our approach involves creating realistic synthetic images that mimic actual X-ray projections, enhanced by incorporating noise profiles derived from real X-ray images. Two deep learning networks, based on the U-Net architecture with a VGG-16 backbone, were trained exclusively on these synthetic datasets to segment PCB junctions and traces. The results demonstrate the effectiveness of this synthetic data-driven approach, with the networks achieving high Jaccard indices on real PCB X-ray images. This study not only offers a scalable and cost-effective alternative for dataset generation in microelectronics but also highlights the potential of synthetic data in training models for complex image analysis tasks, suggesting broad applications in various domains where data scarcity is a concern.
Reference30 articles.
1. Asadizanjani, N., Shahbazmohamadi, S., Tehranipoor, M., and Forte, D. (2015, January 1–5). Non-destructive pcb reverse engineering using x-ray micro computed tomography. Proceedings of the ISTFA 2015, Portland, OR, USA.
2. Pasunuri, A., Jessurun, N., Dizon-Paradis, O.P., and Asadizanjani, N. (2021, January 13). A comparison of neural networks for pcb component segmentation. Proceedings of the 2021 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), Virtual.
3. Li, W., Esders, B., and Breier, M. (2013, January 29–31). SMD segmentation for automated PCB recycling. Proceedings of the 2013 11th IEEE International Conference on Industrial Informatics (INDIN), Bochum, Germany.
4. Li, D., Li, C., Chen, C., and Zhao, Z. (2020). Semantic segmentation of a printed circuit board for component recognition based on depth images. Sensors, 20.
5. Deep Siamese semantic segmentation network for PCB welding defect detection;Ling;IEEE Trans. Instrum. Meas.,2022