AI-Based Semiconductor Defect Detection Using Edge Computing and Computer Vision
SHARAN N · Aug 10, 2026
Abstract Semiconductor manufacturing demands extremely precise inspection because tiny defects can greatly impact the performance, reliability, and yield of chips. Traditional inspection methods often rely on rule-based image processing, manual checks, or centralized computing, which can struggle to handle the growing volume and complexity of high-resolution images. This paper introduces an AI-powered approach to detect defects in semiconductor wafers by combining computer vision, deep learning, and edge computing. Using a lightweight neural network running directly on edge devices, the system processes and analyzes inspection images locally. It can identify and classify various defect types like particles, scratches, pattern issues, cracks, bridging, and missing structures. By handling the data on-site instead of sending large images to a central server, the system reduces delays, bandwidth needs, and dependency on network reliability. Designed for near-real-time use in manufacturing, this approach aims to deliver fast, accurate defect detection while efficiently using computing resources. We evaluate the system’s performance with metrics such as accuracy, precision, recall, F1-score, and inference speed. Overall, this research shows how combining AI with edge computing offers a scalable and responsive solution to improve semiconductor defect inspection. Keywords: Semiconductor Manufacturing, Defect Detection, Artificial Intelligence, Computer Vision, Edge Computing, Deep Learning, CNN, Wafer Inspection, Machine Vision, Industrial AI.