Prediction of Multiple Cell Upset Induced by Heavy Ions in a 90 nm Bulk SRAM

Correas, V.; Saigné, Frédéric; Sagnes, B.; Wrobel, Frédéric; Boch, J.; Gasiot, Gilles; Roché, Philippe

doi:10.1109/tns.2009.2013622

Cited by 40 publications

(13 citation statements)

References 14 publications

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“…Another observation drawn from Figure 6 (top) is a significant decrease in cross-section prediction at low-LET region. The predicted results are fit to the experimental results in [26]. At 5-10 MeV cm 2 /mg, the improved model is also reasonably fit to the experimental results.…”

Section: Nm Sramsupporting

confidence: 59%

“…Based on the proposed approach, the predicted cross sections are plotted in Figure 6. The experimental heavy ion upset cross sections in [26,27] are also plotted in the same diagram for comparison.…”

Section: Nm Srammentioning

confidence: 99%

“…The estimated cross sections from two models (improved model and previous one in [14]) and experimental heavy ion upset cross sections in [26,27] are plotted in Figure 6 (bottom). Besides, the data at low-LET region are zoomed in for comparison in the same figure (top).…”

Section: Nm Srammentioning

confidence: 99%

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Prediction of low-LET ion induced single event upset cross sections for advanced SRAM

Zhou

2013

Journal of Nuclear Science and Technology

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This paper describes a simple circuit-level simulation-based approach to predict single event upset cross section induced by low-linear energy transfer (LET) ions for advanced bulk static random access memory (SRAM). A basic Simulation Program with Integrated Circuit Emphasis (SPICE) model with effective collection depth considered is developed for performing single event analysis quickly and efficiently. Through this circuit-level simulation model, radiation effects can be shown as the SPICE-simulated curve of LETs versus the corresponding affected distances, which are used for upset cross-section prediction. Furthermore, a fine-grain geometric model for cross-section prediction with fine sensitivity coefficient considered is utilized in the prediction. The calculated results based on this method are in good agreement with experimentally measured results reported for six-transistor SRAM fabricated in 90 nm and 65 nm process technologies.

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Section: Nm Sramsupporting

confidence: 59%

Section: Nm Srammentioning

confidence: 99%

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Prediction of low-LET ion induced single event upset cross sections for advanced SRAM

Zhou

2013

Journal of Nuclear Science and Technology

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“…The efficiency of this algorithm (known with name of Single Error Correction /Double Error Detection) can be extremely high, detecting almost all soft errors, revealing that SEUs come from single strikes. This condition is likely to be unsatisfied in a close future with the occurrence of a new failure mode as the Multiple Bit Upset (MBU) [16][17][18][19]. MBU is defined as several adjacent simultaneous bit swaps due to a unique particle hit or to several secondary ions.…”

Section: Amentioning

confidence: 99%

Neutron detection through an SRAM-based test bench

Dilillo

Wrobel

Galliere

et al. 2009

2009 3rd International Workshop on Advances in Sensors and Interfaces

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In this paper, we propose a technique for the detection of neutrons that relies on the sensitivity of SRAM cells to particle radiation. In particular, we introduce a system based on a memory test bench that records the neutron reactions in the memory array. This system allows a good flexibility from different points of view. It is conceived to be modular, programmable, low power consuming and portable. Consequently, it can operate in various experimental conditions such as under artificial sources of particles as well as in natural ambience, from the earth surface to spatial environment. The system is also independent of the type of memory, allowing the use and the study of the interaction between particles and electronic devices built with different technologies.

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“…Indeed, "current injection" approach does not take into account internal changes generated within the device by the voltage variation induced in the circuit by the particle generation. So, the SER could be underestimated [10,11]. However, "current injection" provides a first order modeling necessary for quick analysis of thousand of cases necessary to perform SER prediction.…”

Section: Introductionmentioning

confidence: 99%