Soft Error Rate Predictions for Terrestrial Neutrons at the 3-nm Bulk FinFET Technology

Xiong, Y.; Chiang, Yueh Tao; Pieper, Nicholas J.; Ball, D. R.; Bhuva, B. L.

doi:10.1109/irps48203.2023.10117896

Cited by 1 publication

(2 citation statements)

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“…Yoni et al constructed a comprehensive 3D TCAD model using the layout structure of D flip-flops. They subsequently simulated the circuit's response when the D flip-flop cell was exposed to terrestrial neutrons [17]. Xu et al utilized TCAD simulation tools to investigate the mechanism of soft errors in standard instances.…”

Section: Ser Evaluation Overviewmentioning

confidence: 99%

“…To mitigate soft errors in integrated circuits for space applications, it is crucial to evaluate the soft error rate (SER) during the circuit design phase. In previous works, several circuit-level evaluation approaches have been proposed to investigate the SER of integrated circuits [11][12][13][14][15][16][17][18][19][20][21]. These works have proposed many accurate models to generate transient pulses in circuit nodes.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Soft-Error-Rate Evaluation for Large-Scale Integrated Circuits

Song,

Shao,

Chi

et al. 2023

Electronics

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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.

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Section: Ser Evaluation Overviewmentioning

confidence: 99%