TITLE: FPIC: A Novel Semantic Dataset for Optical PCB Assurance 

ABSTRACT: 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.
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.
FPIC: A Novel Semantic Dataset for Optical PCB Assurance
http://arxiv.org/abs/2202.08414v2