Low-Swing Differential Conditional Capturing Flip-Flop for LC Resonant Clock Distribution Networks
Bibliographic record
Abstract
In this paper we introduce a new flip-flop for use in a low- swing LC resonant clocking scheme. The proposed low-swing differential conditional capturing flip-flop (LS-DCCFF) operates with a low-swing sinusoidal clock through the utilization of reduced swing inverters at the clock port. The functionality of the proposed flip-flop was verified at extreme corners through simulations with parasitics extracted from layout. The LS-DCCFF enables 6.5% reduction in power compared to the full- swing flip-flop with 19% area overhead. In addition, a frequency dependent delay associated with driving pulsed flip-flops with a low-swing sinusoidal clock has been characterized. The LS-DCCFF has 870 ps longer data to output delay as compared to the full-swing flip-flop at the same setup time for a 100 MHz sinusoidal clock. The functionality of the proposed flip-flop was tested and verified by using the LS-DCCFF in a dual-mode multiply and accumulate (MAC) unit fabricated in TSMC 90-nm CMOS technology. Low-swing resonant clocking achieved around 5.8% reduction in total power with 5.7% area overhead for the MAC.
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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".