Statistical model for ring oscillator phase noise variability accounting for within-die process variation
Bibliographic record
Abstract
Phase noise is one of the most restricted specifications in oscillators, especially ring oscillators. Phase noise will exhibit large fluctuations around its nominal value due to the increased process variation with technology scaling. These fluctuations will cause some fabricated ring oscillators not to meet the phase noise constraint and, hence, result in yield loss. This yield loss is expected to become worse especially for sub-90-nm technology nodes. In this paper, an analytical model for the phase noise variability in ring oscillators is proposed. The proposed model has been verified using Monte Carlo SPICE simulations for an industrial 65-nm CMOS technology and is found in good agreement. The model shows that for the commonly used differential-pair-based ring oscillators, the main contribution in phase noise variability comes from the differential pair tail transistor. It also shows that the phase noise variability is reduced as the supply voltage increases. These results can be used to mitigate the phase noise variability and improve the yield through proper sizing of the tail transistor or higher supply voltage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".