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.

Soft errors have been a severe threat to the reliability of modern integrated circuits (ICs), making hardened latch designs indispensable for masking soft errors with redundancy. However, the added redundancy also masks production defects as soft errors; this makes it hard to detect defects in hardened latches, thus significantly reducing their reliability. Our previous work proposed the scan-test-aware hardened latch (STAHL) design, the first for addressing the issue of low defect detectability of hardened latch designs. However, STAHL still suffers from two problems: 1) it is not self-resilient to soft errors and 2) a STAHL-based scan design requires one additional control signal. This article proposes a high defect detectable and single-event-upset (SEU)-resilient robust (HIDER) latch to address the issues of the low defect detectability of existing hardened latches and the STAHLs lack of SEU-resilient capability. Two scan designs [HIDER-based scan-cell-S (HIDER-SC-S) and HIDER-based scan-cell-F (HIDER-SC-F)], as well as two corresponding test procedures, are proposed to fully test HIDER latch with only one control signal. Simulation results show that the HIDER latch achieves the highest defect coverage (DC) in both single latch cell detection and scan tests among all existing hardened latch designs. In addition, the HIDER latch has much lower power and a smaller delay than STAHL.

This paper proposes a novel Test Point Insertion (TPI) strategy to enhance the testability for multi-cycle Built-In Self-Test (BIST) for logic circuits. The approach leverages Multivariate Temporal-Spatial Graph Convolutional Neural Networks (MTS-GCN) and Reinforcement Learning to identify optimal Test Points (TPs). The proposed TPI method treats the testability information of a logic circuit as time-series data and employs Multivariate Time-Series Graph Neural Networks (MTGNN) to capture the relationship between the circuit's structural (spatial information) attributes and the temporal variability of signal line testability across capture cycles. A subsequent Multi-Layer Perceptron (MLP) computes the metric for each signal line to pinpoint potential TPs based on the extracted temporal-spatial features. Experimental evaluation based on benchmark circuits confirms the efficacy of the proposed model, which is trained with Deep Q-Networks (DQN), in improving the fault detection for multi-cycle logic BIST.

Ensuring the correct operation of modern VLSI circuits within safety-critical systems is essential since modern technology nodes are more susceptible to Early-Life Failures (ELFs) and radiation-induced Soft-Errors (SEs). Tackling both of these challenges leads to contradicting design requirements: Effective in-field ELF detection requires online-monitoring or periodic built-in self-testing with excellent cell-internal defect coverage. SE-hardened latch designs, however, are less testable because they are designed to mask cell-internal failures. We propose BiSTAHL, a new SE-hardened scan-cell design that is fully built-in self-testable for both production defects and ELFs.

As modern technology nodes become more susceptible to soft errors, many radiation hardened latch designs have been proposed. However, redundant circuitry used to tolerate soft errors in such hardened latches also reduces the test coverage of cell-internal manufacturing defects. To avoid potential test escapes that lead to soft error vulnerability and reliability issues, this paper proposes a novel Scan-Test-Aware Hardened Latch (STAHL). Simulation results show that STAHL has superior defect coverage compared to previous hardened latches while maintaining full radiation hardening in function mode.

As modern technology nodes get more and more susceptible to soft-errors, various hardened latch cells have been proposed. The added redundancy used to tolerate transient faults in the field at the same time reduces the test coverage of cell-internal production defects. Moreover, the test escapes reduce the soft-error tolerance of the defective latches. This work introduces a new soft-error vulnerability metric called Post Test Vulnerability Factor that correctly measures the added vulnerability to transiant frults such as particle strikes caused by undiscovered production defects within hardened latches.