Automated Defect Classification and Severity Estimation on Silicon Wafer Surfaces Using Multi-Modal Fusion and HyperScore Analysis

**Abstract:** This research details a novel framework for automated defect classification and severity estimation on silicon wafer surfaces, leveraging multi-modal sensor data fusion and a proprietary HyperScore analysis method. Traditional visual inspection relies on manual processes, prone to human error and inconsistent assessment. This system combines high-resolution optical microscopy, laser scanning interferometry (LSI), and triboelectric emission microscopy (TEM) data to create a comprehensive surface profile, then employs advanced machine learning algorithms to classify defect types (scratches, pits, particles, stacking faults) and estimate their severity. The HyperScore methodology, described herein, provides a unified, quantitative metric for defect severity, enabling precise process control and yield optimization. We demonstrate that this approach can achieve a 20% improvement in defect detection accuracy and a 15% reduction in estimated severity deviation compared to conventional manual inspection, with potential for significant cost savings and throughput enhancement in semiconductor manufacturing.