FPIC: A Novel Semantic Dataset for Optical PCB Assurance
This work addresses a data gap for researchers and practitioners in hardware security and PCB fabrication, but it is incremental as it primarily adds a dataset without novel methods.
The authors tackled the lack of labeled ground truth data for machine learning-based optical PCB assurance by introducing the FPIC dataset, which provides a new semantic dataset to support hardware security methodologies.
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.