Functional Fault Impact Probability Prediction using Spatio-Temporal Graph Convolutional Network

Logic-level defects that escape manufacturing tests pose reliability risks in modern systems and require functional testing to identify their activation and propagation behaviors. However, effective functional testing is limited by the high cost of long-cycle fault simulations. To address this challenge, we propose a Spatio-Temporal Graph Convolutional Network framework to efficiently and accurately predict the Fault Impact Probability on the circuit's function cross-over multiple function cycles, enabling rapid quantitative assessment of functionally possible faults. Our method represents gate-level netlists as spatio-temporal graphs, capturing both structural connectivity and short-range signal-propagation dynamics. With dedicated spatial and temporal encoders, the proposed ST-GCN enables accurate prediction of multi-cycle circuit-level FIP. Experiments on ISCAS’89 benchmarks show that the approach reduces fault-simulation cost by over an order of magnitude while maintaining high accuracy (mean absolute error as low as 0.024 for 5-cycle predictions). The framework supports both testability-metric-based and simulation-based feature construction, enabling a tunable balance between efficiency and accuracy. A case study on test point selection further demonstrates that using predicted FIPs to guide observation-point placement improves the detectability of multi-cycle, hard-to-detect circuit-level faults. Overall, this work provides a scalable solution for circuit-level multi-cycle fault-impact assessment and can be readily integrated into functional test generation and other Electronic Design Automation workflows.