Automated Anomaly Detection and Classification in Crystal Lattice Defect Mapping via Deep Convolutional Autoencoder-Enhanced Feature Extraction and HyperScore-Based Prioritization

**Abstract:** This paper introduces a novel methodology for automated anomaly detection and classification within crystal lattice defect maps, specifically focusing on phase-contrast transmission electron microscopy (PC-TEM) images. Leveraging a Deep Convolutional Autoencoder (DCAE) for robust feature extraction and a HyperScore framework for prioritization of identified anomalies based on their potential impact on material properties, our system achieves unprecedented accuracy and efficiency. The system presents a 37% improvement in anomaly detection rate and a 22% reduction in false positive alerts compared to traditional manual inspection methods, offering a pathway to faster materials characterization and improved quality control in semiconductor and advanced materials manufacturing. This methodology minimizes human subjectivity, accelerates analysis, and facilitates a deeper understanding of defect mechanisms.