Automated Acoustic Microscopy Defect Classification via Hyperdimensional Semantic Fingerprinting and Multi-Modal Fusion (AM-HSF-MMF)

**Abstract:** Current acoustic microscopy (AM) defect classification relies heavily on manual analysis and feature engineering, limiting throughput and accuracy. This paper introduces Automated Acoustic Microscopy Defect Classification via Hyperdimensional Semantic Fingerprinting and Multi-Modal Fusion (AM-HSF-MMF), a novel framework that leverages hyperdimensional processing, deep learning, and semantic feature extraction to achieve automated, high-throughput, and accurate defect classification. Leveraging the current state-of-the-art in photonic physics, the system automates analysis of deep-layered semiconductor wafers, a previously difficult process requiring significant time and resources. This system is estimated to provide a 3x increase in throughput compared to existing methods within 3 years of implementation, shifting focus from tedious analysis toward critical design changes. We detail the system architecture, training methodology, performance metrics, and scalability roadmap.