A decision-level fusion hybrid deep learning framework for high-precision automated optical inspection of VCSEL semiconductor devices

This paper presents a hybrid deep learning framework for the automated optical inspection (AOI) of vertical cavity surface-emitting laser (VCSEL) semiconductor devices. Manual inspection is limited by subjective inconsistency and operator fatigue, while high-end commercial AOI systems impose substantial costs that are often impractical for small and medium sized manufacturers. The proposed system adopts a decision-level fusion architecture in which defect-specific binary classifiers are aggregated through an OR-gate. This design prioritizes recall so that any defect flagged by at least one sub-classifier triggers rejection, reducing the risk of defect escape. An industrial dataset of 22, 410 images with severe class imbalance (e.g., crack defects comprising less than 0.4% of all labels) was used for training, with targeted augmentation applied to minority classes. Five fold cross-validation yielded an overall accuracy of 98.7% and an F1-score of 93.5%. Deployment on an active production line reduced per-unit inspection time from 17.7 s to 1.5 s (an 89% reduction) and recorded a secondary defect escape rate of 0.00%.