Using MRED to Screen Multiple-Node Charge-Collection Mitigated SOI Layouts

Black, Jeffrey D.; Dame, Jeff A.; Black, Dolores A.; Dodd, P.E.; Shaneyfelt, M.R.; Teifel, John; Salas, Joseph G.; Steinbach, Robert; Davis, Michaël I.; Reed, Robert A.; Weller, Robert A.; Trippe, James M.; Warren, K.M.; Tonigan, Andrew M.; Schrimpf, Ronald D.; Marquez, Richard S.

doi:10.1109/tns.2018.2882944

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…The first reason is that the proposed approach does not consider the transient pulse reshaping and reconvergence. Transient pulse reshaping or reconvergence results in a change in the width of the transient pulse, which in turn change the data value of T width in Equation (11). Circuit-level simulations are used to investigate the difference in circuit A at low LET values.…”

Section: Ser Evaluation Results Comparisonmentioning

confidence: 99%

“…For the proposed approach, a machine learning model (Equations ( 4)-( 6)) is utilized to calculate the probability of transient pulse propagation. Subsequently, the transient pulse is captured using the transient pulse probability equation (Equation (11)). The proposed method can determine the soft error of the circuit solely through equation calculations.…”

Section: Ser Evaluation Results Comparisonmentioning

confidence: 99%

“…Finally, it simulates the transient response of the circuit using additional simulation tools and determines whether a soft error occurs. Many Monte Carlo-based evaluation approaches have been proposed to evaluate the SER of circuits, such as MRED [11], MUSCA SEP3 [22], PHITS-HyENEXSS [19], and IRT [20].…”

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 Results Comparisonmentioning

confidence: 99%

Section: Ser Evaluation Results Comparisonmentioning

confidence: 99%