Abstract: AI-Based Macro Wafer Defect Detection System for Automated Inspection in Semiconductor Manufacturing This abstract presents the development and deployment of an AI-powered macro wafer defect detection system that automates and optimizes defect identification and classification using advanced deep learning-based object detection techniques. The system integrates a custom-designed mechanical apparatus and optical inspection architecture, enabling simultaneous imaging of both wafer sides—significantly enhancing inspection accuracy, repeatability, and speed compared to manual or semi-automated methods.
Designed for scalability and factory-wide deployment, this solution detects macro-level anomalies such as particle contamination and surface scratches on both the frontside and backside of wafers. By leveraging dual-sided imaging and real-time AI inference, the system reduces human dependency, shortens inspection cycle time, and improves resource utilization by saving 5–10% of high-end scanning equipment capacity.
Mechanically, the system features dual Load Ports for efficient wafer handling, a precision robot arm, alignment translator, and an advanced optical scanner. The hardware is controlled via an intuitive user interface that offers real-time views of front and back wafer surfaces, automatic defect localization, and detailed metadata reporting, including defect location, type, and severity. The interface also allows streamlined navigation and integration into MES (Manufacturing Execution System) workflows.
The modular and intelligent design of the inspection system ensures flexible integration into existing production lines while maintaining strict yield and quality standards. Through continuous model optimization and adaptive learning, the system delivers robust performance in diverse operational scenarios, making it a highly relevant and effective solution for enhancing inspection capacity and reducing cost in next-generation semiconductor fabs.
Keywords: Macro Wafer Inspection, Automatic Optical Inspection (AOI), Deep Learning, AI Defect Detection, Semiconductor Yield Enhancement
