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 (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DQ</sub> ) delays are used in a matched-delay skew compensation technique. The T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DQ</sub> delay 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".