Automated Wafer-Level Packaging Defect Classification via Multi-Modal Data Fusion and HyperScore Evaluation

**Abstract:** The semiconductor industry faces increasing pressure to improve yield and reduce costs in wafer-level packaging (WLP). Current defect detection methods are often limited by the specificity of inspection techniques and the challenges of correlating disparate data streams. This paper introduces a novel framework leveraging multi-modal data ingestion, semantic decomposition, and a hyper-score evaluation system to achieve automated WLP defect classification with unprecedented accuracy and robustness. Our approach combines optical microscopy imagery, infrared thermography data, and high-resolution electrical probe data to create a comprehensive "digital twin" of the wafer, enabling accurate and proactive defect mitigation strategies. The system predicts defect occurrence and impact within a 5-year framework, and is ready for immediate implementation.