Schematic-Aware PCB Inspection Using Computer Vision and Deep Learning for Trace, Solder, and Net-Level Fault Detection

With the increasing complexity and miniaturization of printed circuit boards (PCBs), conventional inspection techniques are no longer sufficient to ensure high manufacturing yield and reliability. This paper presents an intelligent computer vision and machine learning–based framework for automated detection of hardware trace defects, soldering faults, missing components, and layout–schematic mismatches in PCB assemblies. The proposed system performs schematic-aware inspection by aligning captured PCB images with corresponding design netlists and layout data, enabling precise verification of routing continuity, component placement, and interconnect integrity. Advanced image preprocessing, feature extraction, and deep learning–based object detection and classification models are employed to identify subtle manufacturing defects with high accuracy. Experimental results on real PCB datasets demonstrate a significant improvement in defect-detection accuracy and a substantial reduction in false positives compared to conventional rule-based inspection methods. The proposed approach enables fast, non-contact, and scalable PCB inspection, making it highly suitable for next-generation electronics manufacturing, automated quality assurance, and predictive fault analysis.