Experimental Evaluation of Jitter Reduction Methods for Multi-Gigahertz Test
Bibliographic record
Abstract
This paper describes three methods for reducing random jitter (RJ) in Multi-GHz electronic test instruments. These are based on real-time averaging of periodic signals (clocks or reference signals). In each method, N multiple signals are phase-aligned and averaged to reduce jitter by (1/N)1/2. The first method uses multiple phase-locked sources and is evaluated at 1 GHz and 10 GHz. The second method uses integer-cycle delayed copies of a single source and has been shown effective at 4 GHz. A novel third method is introduced and characterized up to 10 GHz using multiple tuned delay line stubs. The three methods can be used individually or in various combinations as well as with traditional techniques (e.g., PLL-based methods). In one example, RJ~300 fs jitter is achieved using a single stage that combines two of the methods, starting with input RJSource~700 fs. Simulation of multiple-stage configurations suggest that “ultra-low” (100–200 fs) jitter may be feasible.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".