Clock tree structure with reduced wire length using the matched-delay skew compensation technique
Bibliographic record
Abstract
In this paper we propose a new approach to balance skew in the clock network by manipulating the operating speed of the flip-flop. Six versions of the master-slave flip-flop with different data to output (TDQ) delays are used in a matched-delay skew compensation technique. The TDQdelay in each version of the flip-flop was increased by increasing the channel length of transistors in intermediate stages of the flip-flop. Distributing flip-flops according to their delay requirements reduces the effect of clock skew on the outputs of sequentially adjacent flip-flops. Furthermore, it increases skew bounds required by algorithms to balance the skew in the clock distribution network leading to reduced design complexity. Constructing five benchmark clock trees with a Modified Deferred Merge Embedding (MDME) algorithm with four, five, and six versions of the flip-flop shows that the matched-delay skew compensation technique can compensate for a skew up to 15% of the clock period. In addition, matched-delay skew compensation achieves a reduction in total wire length and wire elongation up to 16.6% and 56.8%, respectively, as compared to the traditional DME algorithm with only one 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 | 0.001 |
| 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) | 0.001 | 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".