Pixel level anomaly detection in second harmonic generation microscopy for non destructive defect inspection of AlGaN/GaN heterostructures.

In this study, we present a semi-supervised inspection framework that integrates second harmonic generation (SHG) microscopy with pixel-level anomaly detection (PLAD) for high-sensitivity defect mapping in AlGaN/GaN heterostructures grown on Si (111). SHG is intrinsically sensitive to local inversion symmetry breaking and modulation of the effective second-order nonlinear susceptibility. As a result, spatial variations in the nonlinear optical response serve as a physical contrast mechanism for detecting structural inhomogeneities. The SHG-derived defect features are further supported by correlation with cathodoluminescence (CL) based RDE/NBE defect metrics. Complementary to conventional supervised object-centric detectors such as YOLOv8 and Mask R-CNN, the PLAD approach statistically models nominal lattice patterns without manual annotations. This strategy enables improved sensitivity to weak and spatially diffuse crystalline irregularities. Quantitative evaluation demonstrates substantially improved spatial overlap and detection sensitivity compared with object-based models, resolving more than 5,000 micron-scale defect-affected regions within a single field of view that are largely inaccessible to bounding-box or mask-based architectures. By bridging nonlinear optical microscopy with pixel-level anomaly modeling, the SHG-PLAD framework provides a non-contact strategy for defect-sensitive inspection and offers a useful approach for crystalline defect characterization and quality assessment in AlGaN/GaN heterostructures.