AI-Assisted PCB Design And Optimization: A Step Towards Automated Electronics

Abstract

Modern circuits are complex and fast prototyping is now common, PCB design now relies heavily on automation. Older methods of physical design which depend on manual operations and legacy tech, are less effective for high-performance designs these days. This work assesses if artificial intelligence is able to revitalize PCB design by blending component classification, on-the-spot detection and layout enhancement in a straightforward framework. I used convolutional neural networks (ResNet50, MobileNetV2 and Xception) to classify individual components on PCBs and applied YOLOv5 models to detect those components using a dataset of PCB images on Kaggle. The dataset was cleaned and expanded to guarantee that the model performs well in various situations. For classification, we evaluated using accuracy, precision, recall, F1-score and for detection, using mean Average Precision (mAP), Intersection over Union (IoU) and Frames Per Second (FPS). On the classification side, ResNet50 reached 93.5% accuracy, while YOLOv5l led in detection with a 94.8% mAP@0.5 and an IoU of 88.9%. The AI-inspired layout design improved the metrics of the design by decreasing routing by 28.5% and improving thermal performance by more than 45% than the manual layouts. These results explain that AI-based systems can improve PCB design organization, enhance thermal properties and reduce the effort needed to create new designs. It is seen that AI could greatly benefit small- to medium-sized PCB makers and fast prototyping settings. It would be beneficial to expand the model for use with multi-layer and RF printed circuit boards and to add tools that do electromagnetic and signal integrity analysis.