Low-Power Highly Reliable SET-Induced Dual-Node Upset-Hardened Latch and Flip-Flop
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".