MétaCan
Menu
Back to cohort
Record W2164818635 · doi:10.1109/tns.2011.2123918

Neutron- and Proton-Induced Single Event Upsets for D- and DICE-Flip/Flop Designs at a 40 nm Technology Node

2011· article· en· W2164818635 on OpenAlexfundno aff
T. D. Loveless, S. Jagannathan, Trey Reece, Jugantor Chetia, B. L. Bhuva, Michael W. McCurdy, L. W. Massengill, S-J. Wen, Richard Wong, David J. Rennie

Bibliographic record

VenueIEEE Transactions on Nuclear Science · 2011
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsnot available
FundersTRIUMF
KeywordsDiceUpsetFlip-flopNeutronNode (physics)Single event upsetFLOPSEvent (particle physics)PhysicsCharge sharingProtonSoft errorNuclear physicsElectrical engineeringComputer scienceElectronic engineeringOptoelectronicsCMOSEngineeringDetectorParallel computingOpticsMathematics

Abstract

fetched live from OpenAlex

Neutron- and proton-induced single-event upset cross sections of D- and DICE-Flip/Flops are analyzed for designs implemented in a 40 nm bulk technology node. Neutron and proton testing of the flip/flops show only a 30%-50% difference between D- and DICE-Flip/Flop error rates and cross sections. Simulations are used to show that charge sharing is the primary cause for the similar failures-in-time (FIT) rates. Such small improvement in the single-event performance of the DICE implementation over standard D-Flip/Flop designs may warrant careful consideration for the use of DICE designs in 40 nm bulk technologies and beyond.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.232
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations125
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Nuclear ScienceSame topicRadiation Effects in ElectronicsFrench-language works237,207