Fifteenth International Symposium on Quality Electronic Design 2014
DOI: 10.1109/isqed.2014.6783364
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Methodology to optimize critical node separation in hardened flip-flops

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Cited by 8 publications
(5 citation statements)
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“…Moreover, they are increasingly susceptible to upset by MNCC, particularly on bulk technologies. The large number of sensitive node groups in the DICE latch also makes it difficult to provide adequate critical node spacing to avoid MNCC induced upsets [16]. Temporal filtering the FF inputs has demonstrated effectiveness when combined with a DICE latch at mitigating both SET and SEU [17].…”
Section: B Hardening Techniquesmentioning
confidence: 99%
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“…Moreover, they are increasingly susceptible to upset by MNCC, particularly on bulk technologies. The large number of sensitive node groups in the DICE latch also makes it difficult to provide adequate critical node spacing to avoid MNCC induced upsets [16]. Temporal filtering the FF inputs has demonstrated effectiveness when combined with a DICE latch at mitigating both SET and SEU [17].…”
Section: B Hardening Techniquesmentioning
confidence: 99%
“…A meta-program calls an analysis program that in turn runs the HSPICE simulations for maximum flexibility. It is not strictly necessary to run the analysis on all nodes, since combinationally connected nodes are equivalent [16], but it precludes the possibility of an oversight in this key analysis.…”
Section: A Circuit Simulation Based Hardness Verificationmentioning
confidence: 99%
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“…As presented in the previous Section (II-C), the multi-collection is becoming a strong mechanism in very integrated circuit below 28-nm technology (even for SOI technology) and needs to be modeled. However, because of habits, many works consider only the transistor at "off" state and in some cases an unique source of parasite current induced by the radiation on the circuit nodes [86], [87], [89]. This kind of simplified approach leads to limit the real feedback of the circuit.…”
Section: A Toward Integrated "Framework" For Set Modeling In Electromentioning
confidence: 99%