Automated Defect
Detection in Semiconductor
Problem Statement
Traditional defect inspection processes in semiconductor production often face:
-
Low speed due to manual checking
-
Human error leading to missed defects
-
Limited scalability for large production volumes
These challenges result in lower efficiency, higher costs, and delayed deliveries.
Proposed Solution
A vision-based AI inspection system was developed using Computer vision and Machine learning techniques.
Key features include:
-
Real-time image capturing using high-resolution cameras.
-
Automated detection of surface defects such as cracks, scratches, and contamination.
-
Instant defect alerts and classification.
-
Seamless integration with existing production lines.
Implementation Steps
-
Data Collection – High-quality wafer and chip images were collected with both defective and defect-free samples.
-
Model Training – A deep learning model was trained to accurately identify and classify various defect types.
-
System Integration – The model was deployed on the production line for real-time monitoring.
-
Continuous Improvement – Accuracy improved with ongoing data feedback.
Results
-
-
Detection Accuracy: Increased to 98%
-
Inspection Time: Reduced by 60%
-
Production Yield: Improved by 15%
-
Operational Cost: Reduced due to fewer manual inspections and lower product rejection rates.
-
Have a Project in mind?
Project Details
AI Automation
Service: Defect Detection
Technologies: Python Computer vison MERN Stack