Framework for Automatic PCB Marking Detection and Recognition for Hardware Assurance

TL;DR

This paper proposes a framework for automatic detection and recognition of PCB markings to improve hardware assurance, emphasizing data collection and domain knowledge integration for high-accuracy AutoBoM extraction.

Contribution

It introduces a new framework and data collection plan for automatic PCB marking detection, addressing key challenges in PCB text and logo recognition.

Findings

Proposed a data collection strategy for PCB markings.

Outlined a framework for automatic PCB marking recognition.

Discussed future research directions and implications.

Abstract

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.