AI Semiconductor Quality Control

Semiconductor manufacturing tolerates near-zero defect rates — a single contaminated component can fail in the field, triggering recalls and liability claims. Traditional manual inspection is both slow and inconsistent; human inspectors fatigue, and visual defects at micrometer scale are easy to miss under production line conditions.

Golden Sea built a computer vision QC system deployed directly on the production line. High-resolution industrial cameras capture each component as it moves through the inspection station. A YOLOv8 model — fine-tuned on thousands of labeled defect samples provided by the client — classifies each component in under 50ms: pass, or one of 15+ defect categories (surface crack, edge chip, contamination particle, alignment deviation, solder bridge, etc.).

The model was trained using a combination of actual defect samples and synthetic augmentations — rotating, scaling, adjusting lighting, and blending defects onto clean samples to address the class imbalance problem inherent in QC datasets (defects are rare; the model needs enough examples to learn from).

Defects trigger an automatic line stop signal and queue the component for human review or rejection. Each inspection generates a timestamped record: component ID, inspection image, detected class, confidence score, and disposition. Batch reports give QC managers shift-level defect rate trends and early warning when a defect type's frequency starts climbing — often indicating an upstream process parameter drifting out of spec.

Deployed results: 99% defect detection accuracy across all defect categories, and QC throughput improved 25-40% by eliminating manual inspection bottlenecks.