Abstract
In this paper, we present the need for specialized artificial intelligence (AI) for counterfeit and defect detection of PCB components. Popular computer vision object detection techniques are not sufficient for such dense, low inter-class/high intra-class variation, and limited-data hardware assurance scenarios in which accuracy is paramount. Hence, we explored the limitations of existing object detection methodologies, such as region based convolutional neural networks (RCNNs) and single shot detectors (SSDs), and compared them with our proposed method, the electronic component localization and detection network (ECLAD-Net). The results indicate that, of the compared methods, ECLAD-Net demonstrated the highest performance, with a precision of 87.2% and a recall of 98.9%. Though ECLAD-Net demonstrated decent performance, there is still much progress and collaboration needed from the hardware assurance, computer vision, and deep learning communities for automated, accurate, and scalable PCB assurance.
Highlights
Circuit Board (PCB) ComponentPrinted circuit boards (PCBs) are essential components of many contemporary electronic systems, ranging from private sector computers and cell phones to public sector military and medical equipment
Though the dataset can be augmented, there still remain limitations in the PCB assurance domain, especially concerning hardware Trojans (Section 2). These findings suggest that an ideal learning model should be nonlinear, larger than a one-hidden layer neural network (NN), and smaller than VGG16 in terms of the number and the size of layers
We proposed and described a network for PCB component detection to identify malicious, counterfeit, reused, or recycled components
Summary
Printed circuit boards (PCBs) are essential components of many contemporary electronic systems, ranging from private sector computers and cell phones to public sector military and medical equipment. Predominant developments in the outsourcing of PCB manufacturing make boards increasingly vulnerable on a global scale to malicious changes from external entities [1]. Attacks such as hardware Trojan insertions provide backdoor access to sensitive networks and compromise a nation’s infrastructure, military capability, and civil safety. PCB assurance is an important research area in cyber-security. Reference [2] reports that apart from integrated circuits (ICs), resistors, capacitors, and transistors are among the most commonly counterfeited components, these being the most predominant of electronic components on any given PCB; it is, essential to identify and validate these components in any PCB
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