As NVIDIA paints its Physical AI inspection vision, DaoAI has already put it on the line. In December 2025, NVIDIA published a technical article noting that the CNNs (convolutional neural networks) that dominated AOI for a decade have hit a development ceiling.
Three structural limits of traditional CNNs
- High data threshold: each defect type needs thousands of labeled images, and rare defects lack enough samples.
- Limited semantic understanding: the model recognizes images but can't understand context or reason about root cause.
- Constant retraining: switching product lines means re-labeling and re-training, with maintenance cost piling up.
The direction NVIDIA validated
NVIDIA uses a pre-trained visual foundation model, first adapting it to the domain with a million unlabeled factory images, then fine-tuning with a small amount of labeled data. The result: PCB-defect detection accuracy rose from 93.84% to 98.51%.
DaoAI's implementation: built into hardware
DaoAI builds this approach into hardware as a plug-and-play solution:
- Based on a visual foundation model (VGG) architecture
- Trained on 1M+ real SMT factory images
- Specifically domain-adapted for PCBA manufacturing
- Feature extraction in feature space, not pixel space
On the line, the key numbers: programming time −97%, false-call rate −80%, operating cost −60%.
Manufacturers face a choice: adopt a tech stack you have to assemble yourself, or deploy a ready-to-run, continuously self-optimizing inspection system.