4 research outputs found
In-Situ Thickness Measurement of Die Silicon Using Voltage Imaging for Hardware Assurance
Hardware assurance of electronics is a challenging task and is of great
interest to the government and the electronics industry. Physical
inspection-based methods such as reverse engineering (RE) and Trojan scanning
(TS) play an important role in hardware assurance. Therefore, there is a
growing demand for automation in RE and TS. Many state-of-the-art physical
inspection methods incorporate an iterative imaging and delayering workflow. In
practice, uniform delayering can be challenging if the thickness of the initial
layer of material is non-uniform. Moreover, this non-uniformity can reoccur at
any stage during delayering and must be corrected. Therefore, it is critical to
evaluate the thickness of the layers to be removed in a real-time fashion. Our
proposed method uses electron beam voltage imaging, image processing, and Monte
Carlo simulation to measure the thickness of remaining silicon to guide a
uniform delayering processComment: 5 pages, 10 figures, Government Microcircuit Applications & Critical
Technology Conference (GOMACTech) 202
Framework for Automatic PCB Marking Detection and Recognition for Hardware Assurance
A Bill of Materials (BoM) is a list of all components on a printed circuit
board (PCB). Since BoMs are useful for hardware assurance, automatic BoM
extraction (AutoBoM) is of great interest to the government and electronics
industry. To achieve a high-accuracy AutoBoM process, domain knowledge of PCB
text and logos must be utilized. In this study, we discuss the challenges
associated with automatic PCB marking extraction and propose 1) a plan for
collecting salient PCB marking data, and 2) a framework for incorporating this
data for automatic PCB assurance. Given the proposed dataset plan and
framework, subsequent future work, implications, and open research
possibilities are detailed.Comment: 5 pages, 3 figures, Government Microcircuit Applications & Critical
Technology Conference (GOMACTech) 202
FICS PCB X-ray: A dataset for automated printed circuit board inter-layers inspection
Advancements in computer vision and machine learning breakthroughs over the years have paved the way for automated X-ray inspection (AXI) of printed circuit boards (PCBs). However, there is no standard dataset to verify the capabilities and limitations of such advancements in practice due to the lack of publicly available datasets for PCB X-ray inspection. Furthermore, there is a lack of diverse PCB X-ray datasets that encompass images from X-ray Computed Tomography (CT). To address the lack of data, we developed the first comprehensive publicly available dataset, FICS PCB X-ray, to aid in the development of robust PCB-AXI methodologies. The dataset consists of diverse images from the tomographic image domain, along with challenging cases of unaligned, raw X-ray data of PCBs. Further, the dataset contains projection data and the reconstructed volume which is converted into a Tiff stack. Annotated X-ray layer images are also available for image processing and machine learning tasks. This paper summarizes the existing databases and their limitations, and proposes a new dataset, FICS PCB X-ray\u27\u27