Bibliographic record
Abstract
Abstract This paper presents a comprehensive study of phase noise (PN) suppression in LC‐tank oscillators. The goal of this study is to provide designers with the latest techniques for reducing PN in cross‐coupled oscillators. To this end, we begin with a discussion of two prevalent PN models in oscillators: Hajimiri and Demir. We prefer the Hajimiri model because it does not involve very complicated math, and it offers engineers better insight into designing low‐PN oscillators in the two‐PN close‐in regions in an oscillator spectrum (1/f2 and 1/f3). In 1/f2 region, we show that a need for a large output‐voltage swing leads to Class D and B oscillators, and a large output‐current swing results in Class C oscillators. Also, reduction of the impulse sensitivity functions (ISFs) of an oscillator core can happen in Class F oscillators. A few solutions are presented for mitigating flicker noise up‐conversion, such as adding resistances, controlling the oscillation amplitude, decreasing the conduction angle, guiding the high‐frequency harmonics of current, and shifting the phase of VGS against VDS. We also provide a comparison of recent state‐of‐the‐art literature to show what constitutes a good PN in both 1/f2 and 1/f3 regions in cross‐coupled oscillators. We conclude that a cross‐coupled oscillator can reach the best performances in 1/f3 and 1/f2 PN regions if the oscillator is designed in Class C with the K block and uses the techniques of narrowing the conduction angle, the tail inductor, and the modified tank simultaneously.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".