A novel clock distribution and dynamic de-skewing methodology
Bibliographic record
Abstract
In present day VLSI ICs, intra-die processing variations are becoming harder to control, resulting in a large skew in the clock signals at the end of the clock distribution network. We describe a buffered H-tree technique to distribute the clock signal and to de-skew a clock network. The clock shielding wires (which are connected to GND in normal operation) are, in de-skewing mode, used to selectively return the clock signal for de-skewing, and for serial communication with the clock distribution sites for skew adjustment. Our forward and return clock networks are buffered, with identically sized and co-located wires and buffers. This results in both these networks exhibiting identical delay characteristics in the presence of intra-die process variations. Unlike existing approaches, our method utilizes a single phase detection circuit, and can achieve a very low maximum chip-level clock skew. This skew value is not dependent on the resolution of the phase detector. Further, our technique can be applied dynamically, either at boot time or periodically during the operation of the IC, as necessary. Additionally, our buffered H-tree enables us to implement efficient clock gating by allowing the user to turn off clocks in the distribution network itself, thus disabling entire sections of the clock network. We demonstrate the utility of our technique on a 6-level H-tree clock distribution network. In a clock distribution network which is initially skewed by up to 300ps, our technique can de-skew signals to within 4ps of each other. We show that the total wiring area of our clock distribution and de-skewing methodology is about 35% higher than a traditional H-tree (which does not have a deskewing functionality), while the active logic area overhead is about 25%. The power consumption of our network is 5% lower than that of a traditional H-tree network with no de-skewing functionality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".