A Static, Contention-Free, Low-Power, TSPC Dual-Edge Triggered Flip-Flop
Bibliographic record
Abstract
A dual edge-triggered (DET) flip-flop samples the data on both positive and negative edges of the clock. Hence, it can lead to lower clock power consumption as compared to the single edge-triggered (SET) flip-flop while maintaining the same data throughput. In this paper, we present a static, contention-free, low-power, latch-mux type, true single-phase clock (TSPC) DET flip-flop with only 8 clock transistors, referred to as 8CTSPC-DET. The post-layout simulation results in CMOS 65 nm technology suggest that the proposed 8CTSPC-DET is the most power-efficient DET flip-flop for data activities (DAs) up to 30% compared to the all considered DET flip-flops. For example, at nominal voltage, $\mathrm{CK}=1 \mathrm{GHz}$, and $\mathrm{DA}=10 \%$, the proposed 8CTSPC-DET consumes $\mathbf{1 6 \%, ~} \mathbf{2 8 \%}$, and $\mathbf{1 4 \%}$ less power compared to FS-TSPC-DET, TSPC-SDET, and STC-DET, respectively.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".