Defect Prediction in CWDM Optical Modules Using Multimodal Learning

Reliable defect detection in coarse-wavelength division multiplexing (CWDM) optical modules is critical for ensuring stable high-speed optical communication and minimizing network disruptions. Traditional inspection methods, such as manual video screening and optical testing, are labor-intensive, prone to human error, and often fail to identify underlying defect patterns, thereby necessitating multiple rounds of testing. To overcome these limitations, this paper proposes a deep learning-based multimodal learning framework for defect prediction that integrates manufacturing process data (tabular features) and optical eye diagram images from the module’s receiver. Leveraging the complementary nature of these data sources enhances defect detection performance, enabling the proposed model to surpass conventional single modal alternatives. The model was comprehensively evaluated on a real CWDM module dataset through cross-validation, based on performance metrics such as accuracy and F1-score. The multimodal learning schemes achieved an accuracy of 91.0% and an F1-score exceeding 90%, significantly outperforming both traditional single modal approaches and manual inspection techniques. These results demonstrate the effectiveness of multimodal learning in improving defect prediction accuracy, reducing reliance on repetitive testing, and lowering overall inspection costs. The proposed approach represents a scalable and efficient solution for automated quality control in optical module manufacturing, with potential applications in optical network maintenance and defect detection for other photonic components.