Unprecedented precision and reliability in compound semiconductor defect detection with an AI-driven, high-resolution optical inspection solution
With the increasing volume and demand for smaller, faster, and more power-efficient integrated circuits, compound semiconductors have gained significant importance over silicon. This case study describes a novel implemented solution based on high-resolution images obtained with an automated optical inspection (AOI) system, combined with an artificial intelligence-based approach to defect identification, and classification for the purpose of a stable monitoring of compound semiconductors processing.
In our Case Study you will
- Learn how our AI-based anomaly detection algorithm surpasses conventional threshold-binarized difference image methods, enabling more accurate defect detection and classification
- Discover how features like automated sub-pixel precise setting of dynamic Care Areas and the utilization of the entire sub-tiled high-resolution image information maximize inspection accuracy and efficiency
- Understand how this solution minimizes maintenance effort through Deep Learning Training Data Sets and offers greater flexibility for detecting a wide range of defect types
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