Clustering defects in die-to-die interconnect architectures of three-dimensional storage systems constitute one of the critical factors affecting system reliability. To address this challenge, this paper proposes a real-time diagnostic method based on spatio-temporal error information from on-chip error-correcting codes (OD-ECC). The proposed method leverages error location information from multiple consecutive data reads to identify fault patterns, and innovatively incorporates ECC syndrome as an additional criterion to effectively distinguish between fixed faults induced by clustering defects and random soft errors. In this work, we establish a mathematical model for die-to-die interconnect arrays and clustering defects, derive analytical expressions for recall and precision under varying decision thresholds, and construct a Monte Carlo simulation platform for verification. Simulation results demonstrate that, under conditions of a random error probability p=10^-4 and k = 12 consecutive reads, the proposed method achieves recall rates of 98.97% and 96.24% for 1×2 and 2×2 clustering defects, respectively, with precision rates of 99.74% and 99.16%, respectively—significantly outperforming baseline diagnostic methods that rely solely on location information. By incurring only a marginal loss in accuracy, this approach substantially enhances the detection capability for clustering defects, thereby providing robust support for the reliability design of three-dimensional storage systems.