In this paper, an “S-like” inverter chains (SLIC) test structure is designed for the separation measurement of N-hit and P-hit charge sharing. The heavy-ion experiments were performed in 65-nm bulk dual-well and triple-well technology, and the N-hit and P-hit pulse quenching (PQ) span is attained. The results has verified the ability of the “SLIC” test structure to characterize P-hit and N-hit PQ, and the results also indicate that the N+ deep well can enhance the action extent of N-hit charge sharing greatly though it will reduce the action extent P-hit charge sharing.

Based on three-dimensional (3D) technology computer aided design (TCAD) simulations, the supply voltage and temperature dependence of single-event transient (SET) pulse width in 28-nm fully-depleted silicon-on-insulator (FDSOI) metal-oxide-semiconductor field-effect transistors (MOSFETs) is investigated. FDSOI MOSFETs are symmetry devices with a superior control of the short channel effects (SCEs) and single-event effects (SEEs). Previous studies have suggested that the SET width is invariant when the temperature changes in FDSOI devices. Simulation results show that the SET pulse width increases as the supply voltage decreases. When the supply voltage is below 0.6 V, the SET pulse width increases sharply with the decrease of the supply voltage. The SET pulse width is not sensitive to temperature when the supply voltage is 1 V. However, when the supply voltage is 0.6 V or less, the SET pulse width exhibits an anti-temperature effect, and the anti-temperature effect is significantly enhanced as the supply voltage drops. Besides, the mechanism is analyzed from the aspects of saturation current and charge collection.

We propose an experimental method to investigate the dominant multiple node charge collection mechanism. A transistor array-based test structure is used to distinguish charge collection owing to the drift-diffusion and parasitic bipolar amplification effect. Heavy ion experimental results confirm that drift-diffusion dominates multiple node charge collection at low linear energy transfer (LET). However, the parasitic bipolar amplification effect dominates it at high LET. We also propose simple equations to determine the critical LET which may change the dominant multiple node charge collection mechanism. The calculated LET value is consistent with the heavy ion experimental results.

Transient pulses generated by high-energy particles can cause soft errors in circuits, resulting in spacecraft malfunctions and posing serious threats to the normal operation of spacecraft. For integrated circuits used in space applications, it is necessary to first evaluate soft errors caused by transient pulses. Conventional soft-error-rate evaluation tools are designed to simulate the generation of transient pulses using many accurate models, while the propagation of transient pulses is primarily simulated by circuit-level simulation tools. Due to the limitations of simulation tools, conventional evaluation approaches are limited to the circuit scale. The simulation runtime is unbearable for large-scale integrated circuits. This paper presents an approach for evaluating the soft error rate using machine learning. A back propagation neural network is implemented in the proposed approach. It helps to determine the probability of transient pulse propagation. Compared with the conventional soft-error-rate evaluation results, the proposed approach demonstrates a strong correlation in both trend and magnitude. The average difference between the results obtained using the proposed evaluation method and the experimental results is 23.5%, which is 7.5% higher than that between the results obtained using the conventional evaluation method and the experimental results. Compared to the conventional evaluation method, the proposed approach improves the runtime by an order of magnitude. The proposed approach also benefits the locating of highly sensitive circuit nodes in large-scale integrated circuits. Circuit design and radiation hardening are both useful applications.

The static random-access memory (SRAM) physical unclonable function (PUF) uses the power-up states of stored values to derive the identification code. Its lightweight circuit design makes it suitable for power-sensitive satellites. However, the SRAM PUF is significantly affected by high-energy particles and cosmic rays in space, which causes incorrect output of identity authentication codes and further leading to a degradation in reliability. Recently, the main research works are concerned about the impact of cosmic rays induced total ionizing dose effects on the SRAM PUF, and few reports discuss the high-energy particles induced single event effects on the SRAM PUF. This paper presents the reliability results of planar and FinFET-based SRAM PUFs after high-energy heavy ion experiments. Experimental results indicate that 3%–10% of the SRAM PUF bits change from their original power-on states, which demonstrate that ion-induced microdose effects degrade the reliability of SRAM PUFs. In addition, the three-dimensional technology computer-aided design simulation tool is used to analyze the physical mechanisms of the experimental phenomena. Simulation results show that the heavy ion-induced microdose effect ionizes and produces trapped charges at the silicon/insulation interface, which causes a slight change in transistor leakage current or a slight drift in threshold voltage. The physical mechanisms lead to an off-state current mismatch in the SRAM cell and finally affect the stored values after power-on.