AI-Assisted Component Recognition for Product Lifecycle Assessment in Microelectronics

As microelectronic devices continue to grow in complexity, ensuring the accuracy and reliability of printed circuit board (PCB) manufacturing has become a critical challenge. In particular, verifying the correct placement and classification of components during assembly is essential for quality assurance and defect prevention. This paper introduces an AI-assisted framework for automated inspection of PCB components and their positions, designed to support greater efficiency and consistency in manufacturing processes. The proposed approach integrates high-resolution image acquisition with advanced computer vision models for precise detection and classification of electronic components. To extend beyond simple recognition, large language models (LLMs) are incorporated to enrich inspection results with contextual knowledge, enabling the system to verify whether detected components match their intended specifications and positions. By linking visual inspection with intelligent data interpretation, the framework provides a robust digital tool for monitoring assembly quality. The framework is designed to reduce manual inspection effort, minimize errors, and strengthen the overall reliability of PCB production. By combining computer vision with language models, this approach offers a step toward more automated, scalable, and high-quality inspection processes in modern electronics manufacturing.