Jitter generation and capture using phase-domain sigma-delta encoding
Bibliographic record
Abstract
This article presents techniques and circuits for jitter generation and measurement. The proposed implementations use periodic bit-streams and high-order PLLs to generate the desired phase signal. Here, an arbitrary signal is first encoded using sigma-delta modulation in the digital amplitude-domain and converted to the phase-domain through a digital-to-time converter (DTC) process realized in software. The resulting bit-stream is inputted cyclically to a high-order phase-locked loop (PLL) behaving as a time-domain filter. The parameters of the sigma-delta modulator along with those of the high-order PLL can be traded for one another to achieve maximum performance. The method to generate the sigma-delta encoded phase signal and to design the high-order PLL is presented. A high quality Gaussian jitter signal has been experimentally generated. Also, a setup using DC encoded phase shifts serving as an under-sampling clock to measure jitter with a 50 GHz effective sampling rate has also been experimentally proven. The conciseness and digital nature of the jitter generation scheme together with the jitter measurement architecture makes them easily amenable to a design-for-test framework.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".