FPIC: A Novel Semantic Dataset for Optical PCB Assurance
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Abstract (arXiv)
Outsourced printed circuit board (PCB) fabrication necessitates increased hardware assurance capabilities. Several assurance techniques based on automated optical inspection (AOI) have been proposed that leverage PCB images acquired using digital cameras. We review state-of-the-art AOI techniques and observe a 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 contribute the FICS PCB Image Collection (FPIC) dataset to address this need. Additionally, we outline new hardware security methodologies enabled by our data set.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 62 references that are in the map. First posted on arXiv 17 Feb 2022.
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9 of 62 references.
References · 9
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
- 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
- 2018Context Encoding for Semantic Segmentationcited by 289
- 2017Understanding Convolution for Semantic Segmentationcited by 168
- 2017A Review on Deep Learning Techniques Applied to Semantic Segmentationcited by 122
- 2019Online PCB Defect Detector On A New PCB Defect Datasetcited by 25
- 2019A PCB Dataset for Defects Detection and Classificationcited by 16
- 2021ChangeChip: A Reference-Based Unsupervised Change Detection for PCB Defect Detectioncited by 6
- 2018Data-Efficient Graph Embedding Learning for PCB Component Detectioncited by 4
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Citing papers per year: 2026: 2.
2 papers.