Pattern‐Aware Intelligence Enables Nondestructive, Rapid Quantification of High‐Aspect‐Ratio Silicon Etching

High‐aspect‐ratio (HAR) structures are widely used in microelectromechanical systems (MEMS), electronic devices, and advanced packaging. Accurate characterization of post‐etch microstructural morphology is essential for process control and device reliability. However, existing metrology techniques rely on destructive methods and cannot capture the complete three‐dimensional profile. This study proposes a pattern‐aware intelligent model that transforms pattern‐dependent etching behavior into a physical prior, enabling rapid and minimally destructive reconstruction of HAR structures across pattern scales. A YOLO‐Pose‐based model is developed to automatically extract subpixel geometric features from scanning electron microscopy images, achieving a quantification accuracy of 95.11% with R 2 >0.98 for key dimensions. By incorporating the pattern‐dependent effect in HAR silicon etching as a physical prior, the model creates a topography network that captures etch behavior across feature sizes under identical process conditions. With minimal destructive observations of shallow regions of the wafer, the framework achieves a 93.96% prediction accuracy in the nondestructive retrieval of critical parameters, including etch depth, sidewall angle, and scallop texture, while demonstrating robust cross‐scale generalization across varying layout densities. This approach reduces the data acquisition time to 1 min, enabling near‐real‐time process feedback. It provides a highly interpretable, cost‐effective, and intelligent solution for intelligent manufacturing applications.