Congestion Driven Clock Tree Routing with Via Minimization
Bibliographic record
Abstract
Physical routability constraints such as legal location checking and excessive number of vias are usually ignored in most of the clock tree algorithms. These Constraints could make an abstract clock tree difficult to route in practice and cause important manufacturability and reliability challenges. Therefore the final clock tree layout specifications can be seriously deviated from the expected ones. Vias have major impact on circuit reliability and manufacturing yield. The variability in via resistance is becoming an increasing concern in nanotechnologies. In this thesis a practical frame work is proposed to construct the clock tree network under via constraint. We propose an algorithm that minimizes the number of bends that is closely related to the number of vias. The proposed algorithm is able to construct a zero skew clock tree with at most one bend branch merging. By performing simultaneous wire sizing and clock tree construction, the algorithm effectively reduces the number of bends at the expense of a small increase in capacitance. Furthermore, the number of vias is also controlled by considering a pre-specified pattern to route the internal clock tree edges. The impact of the pattern routing is taken into account in the early clock distribution design phase. We introduce a probabilistic routing demand estimation method to integrate the expected routing demand of the clock net with other clock tree optimization metrics. A new demand driven cost function is exploited in network topology generation as well as branch point embedding stages of a zero skew clock tree algorithm to reduce the number of vias. Our experiments show considerable improvements in the total number of vias. 28% reduction in the number of vias is obtained while the total clock tree wire length is reduced by an average of 8%. The post-routing induced clock skew is also controlled efficiently.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".