A Wide-Tunning-Range Low-Phase-Noise Colpitts Oscillator with Variable Capacitive Feedback
Bibliographic record
Abstract
In this paper, we propose a technique to extend the tuning range (TR) of Colpitts complementary metal-oxide semiconductor (CMOS) voltage-controlled oscillators (VCOs). To achieve a wide tuning range, we use variable capacitive feedback to extend the tuning range while achieving lower phase noise (PN) than that of the conventional Colpitts VCOs. In addition, the structure benefits from the dynamic forward-body self-biased technique to reduce the power consumption and facilitate the start-up condition. As a proof-of-concept, a 28-GHz Colpitts oscillator, which is suitable for 5G wireless applications, is designed and laid out in a 65-nm CMOS process. Post-layout simulations in conjunction with electromagnetic (EM) simulations confirm the improved performance of the proposed technique. Based on the simulation results, the VC O achieves a PN of -115.7 dBc/Hz at 10-MHz offset, and TR of ~34.5% resulting in a figure of merit incorporating the tuning range (FOMt) of -189 dBc/Hz. The circuit consumes 4.3 mW from a 1-V supply and excluding the pads occupies a 0.12 mm2of silicon area.
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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) | 0.000 | 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".