Why Is Deep Learning Challenging for Printed Circuit Board (PCB) Component Recognition and How Can We Address It?

• Main Authors: Mukhil Azhagan Mallaiyan Sathiaseelan, Olivia P. Paradis, Shayan Taheri, Navid Asadizanjani
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Cryptography
• Subjects: hardware assurance; PCB assurance; reverse engineering; bill of materials; AutoBoM; automated optical inspection
• Online Access: https://www.mdpi.com/2410-387X/5/1/9

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.