Automated defect classification in wafer maps is critical for semiconductor yield management and quality control, but pure deep learning models often underperform on rare or spatially subtle defect types and lack interpretability. Handcrafted spatial features can capture physical defect characteristics, yet their integration with modern CNNs is underexplored. We systematically evaluate eight physically motivated spatial descriptors: radial mean and standard deviation, directional entropy, aspect ratio, fail fraction, and zone-wise failure densities (core, mid, edge), by training a lightweight CNN (101k parameters) augmented with each descriptor, both individually and in full combination. To the best of our knowledge, this is the first systematic ablation study to quantify the synergistic effect of fusing physically-informed spatial descriptors with a modern, edge-optimized CNN for this task.On the WM-811K benchmark (eight defect classes, 25,519 labeled wafers), the vision-only baseline achieves 60.0% test accuracy and 0.615 weighted F1. Nearly every single descriptor individually underperforms the baseline, with the best descriptor (fail fraction) reaching only 60.1%. However, the full fusion of all eight descriptors significantly outperforms the baseline, reaching 72.7% accuracy (+12.7 points) and 0.728 weighted F1. This synergy demonstrates that spatial descriptors provide complementary information that is only realizable in combination.Per-class analysis reveals that the combination model substantially improves challenging classes: Donut F1 rises from 0.207 to 0.493, Edge-Loc from 0.384 to 0.672, and Center from 0.579 to 0.760. However, the Loc class remains challenging for all models, likely due to its diffuse spatial patterns.