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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. [ABSTRACT FROM AUTHOR]

The heavy ion-induced sensitive area is an essential parameter for space application integrated circuits. Circuit Designers need it to evaluate and mitigate heavy ion-induced soft errors. However, it is hard to measure this parameter due to the lack of test structures and methods. In this paper, a test method called TAISAM was proposed to measure the heavy ion-induced sensitive area. TAISAM circuits were irradiated under the heavy ions. The measured sensitive areas are 1.75 μ m 2 and 1.00 μ m 2 with different LET values. TAISAM circuits are also used to investigate the layout structures that can affect the sensitive area. When the source region of the target transistor is floating, the heavy ion-induced sensitive area decreases by 28.5% for the target PMOS transistor while it increases by more than 28% for the target NMOS transistor. When the well contacts are added, the heavy ion-induced sensitive area decreases by more than 25% for the target PMOS transistor while it remains unchanged for the target NMOS transistor. Experimental results directly validate that the two structures significantly affect the heavy ion-induced sensitive area. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]