Contribution of Thermal Neutrons to Soft Error Rate

Weulersse, C.; Houssany, Sabrine; Guibbaud, N.; Segura-Ruíz, Jaime; Beaucour, J.; Miller, F.; Mazurek, M.

doi:10.1109/tns.2018.2813367

Cited by 38 publications

(26 citation statements)

References 23 publications

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“…The contribution of the Boron concentration interacting with thermal neutrons was not considered in the calculation because it depends on the surrounding device material and this information is not available. Nevertheless, recent works have discovered that the contribution of the terrestrial thermal neutrons cannot be neglected since it can be equivalent to that from the highenergy neutron part [36]. Future works based on thermal neutrons tests and simulations are being currently conducted.…”

Section: A Musca Sep3 Predictionsmentioning

confidence: 99%

Single Event Upsets Under 14-MeV Neutrons in a 28-nm SRAM-Based FPGA in Static Mode

Fabero

Mecha

Franco

et al. 2020

IEEE Trans. Nucl. Sci.

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A sensitivity characterization of a Xilinx Artix-7 FPGA against 14.2 MeV neutrons is presented. The content of the internal SRAMs and flip-flops were downloaded in a PC and compared with a golden version of it. Flipped cells were identified and classified as cells of the configuration RAM, BRAM, or flip-flops. SBUs and MCUs with multiplicities ranging from 2 to 8 were identified using a statistical method. Possible shapes of multiple events are also investigated, showing a trend to follow wordlines. Finally, MUSCA SEP3 was used to make assesment for actual environments and an improvement of SEU injection test is proposed.

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Section: A Musca Sep3 Predictionsmentioning

confidence: 99%

Single Event Upsets Under 14-MeV Neutrons in a 28-nm SRAM-Based FPGA in Static Mode

Fabero

Mecha

Franco

et al. 2020

IEEE Trans. Nucl. Sci.

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“…the PS, or on the on the AXI module instantiated on the FPGA fabric, which can cause the PS to hang if it fails to perform a proper handshake. The obtained static cross-section and the Failure In Time (FIT) are respectively 4.11 • 10 −10 cm 2 and 2.67, considering New York's flux at sea level (6.5 thermal neutrons / (h • cm 2 )) [12]. 4695 samples were processed by the Binary SVM with radiation-induced errors.…”

Section: Radiation Test Results For the Binary Svmmentioning

confidence: 99%

“…The thermal neutron test has been conducted at the Platform for Advanced Characterisation (PAC-G), hosted by Institut Laue-Languevin (ILL), using the D50 thermal neutron source. Previous papers [12] have demonstrated the relevance of the usage of this equipment to perform reliability testing. It provides a thermal neutron beam with a spectrum ranging from few microelectronvolts to around 100 meV, with a peak around 13 meV.…”

Section: Radiation Test Set-upmentioning

confidence: 99%

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Effects of thermal neutron radiation on a hardware-implemented machine learning algorithm

Trindade

Benevenuti

Létiche

et al. 2021

Microelectronics Reliability

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Hardware-implemented machine learning algorithms are finding their way in various domains, including safety-critical applications. This has demanded these algorithms to perform correctly even in harsh environmental conditions, such as in avionics altitudes. Support Vector Machine (SVM) is an important Machine Learning that has been target of hardware implementation in recent years. This is the first work to asses both Binary and Multiclass SVMs under thermal neutron radiation, a type of particle noticeably present in high altitudes. A fault injection campaign along with a radiation test with the D50 thermal neutron source, at the Intitut Laue-Languevin, has been performed. The results show a high intrinsic fault tolerance for both varieties of the SVM algorithm, especially for the Multiclass SVM.

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“…The atmospheric spectrum also contains fast neutrons that can be slowed down to thermal energies through capture and scattering processes in materials containing hydrogen. This may be a particular problem for avionic systems, where the high-energy neutron rate is ∼300 times higher than sea level and the proximity of large amounts of thermalizing material (fuel) is present [18]. Thermal neutrons may also be present but uncharacterized in broad spectrum test facility beams.…”

Section: Introductionmentioning

confidence: 99%

Thermal Neutron-Induced Single-Event Upsets in Microcontrollers Containing Boron-10

Auden

Quinn

Wender

et al. 2020

IEEE Trans. Nucl. Sci.

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Single-event upsets (SEUs) were measured in thermal neutron-irradiated microcontrollers with 65-and 130-nm-node static random-access memories (SRAMs). The suspected upset mechanism is charge deposition from the energetic byproducts of 10 B thermal neutron capture. Although elemental analysis confirmed that both microcontrollers contain 10 B, only the 65-nm node microcontroller exhibited a strong response to thermal neutrons. Monte Carlo simulations were performed to investigate the effects of 11 B enrichment on thermal neutroninduced SEUs in a 65-nm SRAM node when boron is present in the p-type well, p-type source and drain, or tungsten plug. Simulations indicate that the byproducts of 10 B(n, α) 7 Li reactions are capable of generating sufficient charge to upset a 65-nm SRAM. The highest amount of charge deposition from 10 B(n, α) 7 Li reaction byproducts occurs when natural boron is used to dope the p-type source and drain regions. Simulations also show that the SEU cross section is nonnegligible when 11 B-enriched boron is used for doping.

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Contribution of Thermal Neutrons to Soft Error Rate

Cited by 38 publications

References 23 publications

Single Event Upsets Under 14-MeV Neutrons in a 28-nm SRAM-Based FPGA in Static Mode

Single Event Upsets Under 14-MeV Neutrons in a 28-nm SRAM-Based FPGA in Static Mode

Effects of thermal neutron radiation on a hardware-implemented machine learning algorithm

Thermal Neutron-Induced Single-Event Upsets in Microcontrollers Containing Boron-10

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