Radiation Effects in 3D Integrated SOI SRAM Circuits

Gouker, P.; Tyrrell, Brian; D’Onofrio, Richard; Wyatt, P.W.; Soares, Thiago R. B. S.; Hu, Wenqian; Chen, Chenson; Schwank, J.R.; Shaneyfelt, M.R.; Blackmore, E. W.; Delikat, K.J.; Nelson, Martin E.; McMarr, P.J.; Hughes, H.L.; Ahlbin, J. R.; Weeden-Wright, Stephanie L.; Schrimpf, R.D.

doi:10.1109/tns.2011.2172463

Cited by 17 publications

(16 citation statements)

References 18 publications

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“…In principle, one of the biggest advantages of proton testing is that it allows one to irradiate a complicated multitechnology component [2], or even an entire board or system without having to significantly modify it. Unfortunately, such a test vehicle is likely to use multiple technologies, each with its own SEE modes (some destructive and some not) and each SEE mode with its characteristic SV.…”

Section: Hardness Assurance Implicationsmentioning

confidence: 99%

“…Thus for a given proton energy , a device with sensitive volumes of depth d will generate an expected number of events (2) We chose to be the Weibull form parameterized in terms of the limiting cross section , onset LET, LET , and the Weibull width w and shape s. Observed events will fluctuate about this mean according to the Poisson distribution, so the likelihood L for the GLM will be (3) Ideally, we would maximize L in terms of the parameters. However, proton data usually weakly constrains the SV model, so we likely will be unable to find a single clear best-fit model.…”

Section: Generalized Linear Modelmentioning

confidence: 99%

“…Low-cost missions such as cubesats often find such testing beyond their budgets. Even well-funded missions face issues testing commercial off-the-shelf (COTS) components due to packaging and integration [1], [2]. Some missions wish to fly COTS systems without investigating each component.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of Proton SEE Data as a Proxy for Bounding Heavy-Ion SEE Susceptibility

Ladbury

Lauenstein

Hayes

2015

IEEE Trans. Nucl. Sci.

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We examine use of proton single-event effect (SEE) data to constrain heavy-ion SEE susceptibility. We discuss limitations due to short-range proton recoils, especially for destructive SEE modes. We develop an equivalent linear energy transfer metric that reduces risk that proton data will significantly underestimate SEE susceptibility and suggest a probabilistic model for using proton data to constrain device-sensitive volumes.Index Terms-Heavy ions, probabilistic risk assessment, proton radiation effects, radiation hardness assurance methodology, single-event effect (SEE), single-event latchup (SEL).

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Section: Hardness Assurance Implicationsmentioning

confidence: 99%

Section: Generalized Linear Modelmentioning

confidence: 99%

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Use of Proton SEE Data as a Proxy for Bounding Heavy-Ion SEE Susceptibility

Ladbury

Lauenstein

Hayes

2015

IEEE Trans. Nucl. Sci.

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“…Although 3D SRAM has multi stacked dies, it has been proved that incident particles can reach to each die and induce SET and SEU [4]. Because of the multi dies stacked structure, the SET and SEU producing and transfer becomes much complex in 3D SRAM.…”

Section: Introductionmentioning

confidence: 99%

Design of Analysis Platform Used for Studying Soft Error Characteristic of 3D SRAM

Li¹,

Zhang²,

Deng³

2015

Advances in Intelligent Systems Research

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This paper designs a novel 3D SRAM soft error analysis platform. It is used for studying the soft error characteristic of 3D SRAM stacked by multi dies and guiding the radiation hardened design for 3D SRAM. This platform integrates simulation tools including Geant4, TCAD and Nanosim, data recording and processing tools ROOT, layout processing tools Calibre, and Perl and Shell script used for linking task and analyzing results. After inputting the stacked structure, technology, circuit netlist and layout of 3D SRAM and incident particles information, this platform can simulate the particle striking 3D SRAM automatically, and then we get some kinds of cross sections, error information in a single word and sensitive node distribution for 3D SRAM. A 2 die stacked 3D SRAM based on 65nm CMOS technology is analyzed through this platform.

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“…The results dedicated that upset cross sections for vertically integrated SRAMs are similar for all three dies, but for heavy ions, no results were presented [11]. Peng Li et al researched the impact of heavy ion species and energy on SEE characteristics of 3DIC.…”

mentioning

confidence: 99%

Heavy ion-induced single event upset sensitivity evaluation of 3D integrated static random access memory

et al. 2018

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Abstract：Heavy ions induced single event upset (SEU) sensitivity of three-dimensional (3D) integrated SRAMs are evaluated by using Monte Carlo simulation methods based on Geant4. The cross sections of SEUs and Multi Cell Upsets (MCUs) for 3D SRAM are simulated by using heavy ions with different energies and LETs. The results show that the sensitivity of different die of 3D SRAM has obvious discrepancies at low LET. Average percentage of MCUs of 3D SRAMs rises from 17.2% to 32.95% when LET increases from 42.19 MeV•cm 2 /mg to 58.57 MeV•cm 2 /mg. As for a certain LET, the percentage of MCUs shows a notable distinction between face-to-face structure and back-to-face structure. For back-to-face structure, the percentage of MCUs increases with the deeper die. However, the face-to-face die presents the relatively low percentage of MCUs. The comparison of SEU cross sections for planar SRAMs and experiment data are conducted to indicate the effectiveness of our simulation method. Finally, we compare the upset cross sections of planar process and 3D integrated SRAMs. Results demonstrate that the sensitivity of 3D SRAMs is not more than that of planar SRAMs and the 3D structure can become a great potential application for aerospace and military domain.

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Radiation Effects in 3D Integrated SOI SRAM Circuits

Cited by 17 publications

References 18 publications

Use of Proton SEE Data as a Proxy for Bounding Heavy-Ion SEE Susceptibility

Use of Proton SEE Data as a Proxy for Bounding Heavy-Ion SEE Susceptibility

Design of Analysis Platform Used for Studying Soft Error Characteristic of 3D SRAM

Heavy ion-induced single event upset sensitivity evaluation of 3D integrated static random access memory

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