IC SEE Comparative Studies at UCL and JINR Heavy Ion Accelerators

Akhmetov, A. O.; Bobrovsky, Dmitry V.; Tararaksin, Alexander S.; Petrov, Andrey G.; Kessarinskiy, Leonid N.; Boychenko, Dmitry V.; Chumakov, A. I.; Rousset, A.; Chatry, C.

doi:10.1109/nsrec.2016.7891720

Cited by 13 publications

(2 citation statements)

References 6 publications

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“…Using the commercial K-layout tool, the layout description of the SRAM and FF cells have been extracted in terms of Graphic Data System-II (GDS-II). Based on the netlist and layout of the cells, we have performed a radiation analysis, using our in-house Monte Carlo-based simulation tool, described in detail in [5], using the Heavy Ion Profile related to the Université Catholique de Louvain (UCL) facility [24]. We have performed a simulation of 10,000 particles for each cell and we reported the obtained cross-section in Figure 7.…”

Section: Static Radiation Sensitivity Analysismentioning

confidence: 99%

“…cells, we have performed a radiation analysis, using our in-house Monte Carlo-based simulation tool, described in detail in [5], using the Heavy Ion Profile related to the Université Catholique de Louvain (UCL) facility [24]. We have performed a simulation of 10,000 particles for each cell and we reported the obtained cross-section in Figure 7.…”

Section: Static Radiation Sensitivity Analysismentioning

confidence: 99%

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Analysis of Single Event Effects on Embedded Processor

et al. 2021

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The continuous scaling of electronic components has led to the development of high-performance microprocessors which are even suitable for safety-critical applications where radiation-induced errors, such as single event effects (SEEs), are one of the most important reliability issues. This work focuses on the development of a fault injection environment capable of analyzing the impact of errors on the functionality of an ARM Cortex-A9 microprocessor embedded within a Zynq-7000 AP-SoC, considering different fault models affecting both the system memory and register resources of the embedded processor. We developed a novel Python-based fault injection platform for the emulation of radiation-induced faults within the AP-SoC hardware resources during the execution of software applications. The fault injection approach is not intrusive, and it does not require modifying the software application under evaluation. The experimental analyses have been performed on a subset of the MiBench benchmark software suite. Fault injection results demonstrate the capability of the developed method and the possibility of evaluating various sets of fault models.

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Section: Static Radiation Sensitivity Analysismentioning

confidence: 99%

Section: Static Radiation Sensitivity Analysismentioning

confidence: 99%