A Generative AI-Based System forPCB Testing

Abstract

Printed Circuit Board (PCB) testing has traditionally relied on manual inspection methods, which are often time- consuming, error-prone, and costly. These limitations result in longer processing times, increased production costs, and inconsistent quality assurance. To address these challenges, this paper proposes a Generative AI-Based PCB Testing (GABPT) system designed to automate defect detection and classification in PCBs. The system leverages high-resolution image acquisition, advanced AI-powered image processing, and classification algorithms to identify common defects such as scratches, missing holes, broken paths, mouse bites, short circuits, and absent conductors. Utilizing a Quad ARM Cortex-A72 processor, the system processes captured PCB images in real- time and delivers immediate feedback on defects. The GABPT system aims to reduce human error, accelerate the inspection process, and minimize testing costs. It emphasizes efficient image preprocessing, precise image analysis, and hardware-software integration for reliable defect detection. By automating PCB inspection, the proposed system enhances testing accuracy, promotes cost-effective production, and supports sustainable manufacturing practices, ultimately contributing to the delivery of higher-quality electronic products.