Deploying edge AI and computer vision to identify micro-defects in semiconductor manufacturing, achieving 99.8% accuracy.
99.8%
Defect Detection Accuracy
15%
Increase in Line Speed
$4M
Annual Scrap Savings
The manufacturer relied on human inspectors to identify micro-defects in high-density circuit boards. This process was slow, prone to fatigue-induced errors, and acted as a major bottleneck on the production line. Previous attempts at machine vision failed due to the high variability of lighting conditions on the factory floor.
Engineered a custom convolutional neural network (CNN) trained on thousands of augmented defect images.
Deployed the model on edge devices (NVIDIA Jetson) directly on the assembly line for zero-latency inference.
Implemented an active learning loop where human experts would review edge-case flags, continuously improving the model's accuracy.
Built an executive dashboard tracking defect rates and root-cause analysis across three global factories.
The computer vision system surpassed human accuracy within 8 weeks of deployment, allowing the manufacturer to increase line speed by 15% without sacrificing quality. The system is now being rolled out globally across all production facilities.
