Autonomous, Multilevel Ring Tuning Scheme for Post-Silicon Active Clock Deskewing Over Intra-Die Variations
Bibliographic record
Abstract
Synchronous clock distribution continues to be the dominant timing methodology for VLSI circuit designs. As processes shrink, clock speeds increase, and die sizes grow, an increasingly larger percentage of the clock period is being lost to skew and jitter budgets. Process, voltage, and temperature variations, especially those that are intra-die, increasingly upset the distribution of a synchronized clock signal, even in properly balanced clock tree networks. As a result, active clock deskewing systems are becoming necessary to tune out unwanted clock skew after chip fabrication. This paper first defines the operation of a specially designed phase detector, referred to as an up/down detector (UDD). Next, four of these UDDs are used as links to construct a stable, autonomously locking quadrantal ring tuning (QRT) configuration that effectively joins together four distributed delay-locked loops (DLLs) without the need for any system-level controller. This cyclic, self-controlled, quad-DLL ring tuning technique is then implemented hierarchically to dynamically adjust clock signal delays across an entire chip during normal circuit operation. A simplistic two-level QRT system is presented for a generic H-tree clock distribution network, demonstrating stable locking behavior and more than 50% average reduction in overall clock skew.
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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 | 0.000 | 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) | 0.001 | 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".