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Record W2972617630 · doi:10.1109/cjece.2019.2895047

Low-Power Highly Reliable SET-Induced Dual-Node Upset-Hardened Latch and Flip-Flop

2019· article· en· W2972617630 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsnot available
FundersMichigan Space Grant Consortium
KeywordsFlip-flopUpsetNode (physics)TransistorPower–delay productSoft errorComputer sciencePower (physics)Electrical engineeringMaterials scienceVoltageElectronic engineeringOptoelectronicsCMOSPhysicsEngineering

Abstract

fetched live from OpenAlex

It appears that the relentless pursuit of Moore's law scaling from one generation of process technology to the next increases circuit vulnerability to single-event transient (SET)-induced double-node upset (SEDU). In this paper, we present a novel SEDU-hardened latch. The latch consists of a new 16-transistor (16T) SEDU-hardened storage cell and a C-type output buffer. The latch exhibits 25% lower power consumption, is 81% faster, and also shows 86% lower power-delay product than the existing SEDU-hardened latches. In addition, we present the first SEDU-hardened flip-flop that exhibits negative hold time. The proposed SEDU-hardened flip-flop is 29% faster, consumes 50% lower dynamic power and 25% lower static power, has 45% lower setup time, and uses 27% lower area than the existing partial SEDU-hardened flip-flop.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

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

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 head0.003
GPT teacher head0.157
Teacher spread0.154 · 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