A 2.3-mW 26.3-GHz $G_{m}$ -Boosted Differential Colpitts VCO With 20% Tuning Range in 65-nm CMOS
Bibliographic record
Abstract
This paper presents an architecture for differential Colpitts voltage-controlled oscillators (VCOs) in complementary metal-oxide-semiconductor (CMOS) that utilizes three design techniques to extend the tuning range (TR) of the VCO, while maintaining a low phase noise (PN) and a low power consumption. First, a switched-capacitor bank based on a variable capacitive feedback technique is introduced to achieve a wide TR with a minimal PN degradation. Second, a Gm-boosting technique using interstage inductors is employed to lower the power consumption and relax VCO startup issues. Third, a dynamic forward-body self-biased technique is used to further reduce the power consumption and PN of the proposed structure. As a proof of concept, a 26.3-GHz differential Colpitts VCO is designed and fabricated in a 65-nm CMOS process. Based on the measurement results, the VCO achieves a PN of -122.1 dBc/Hz at 10-MHz offset from the center frequency, and a TR of 20%. The circuit consumes 2.3 mW from a 1-V supply and excluding the pads occupies a 0.22 mm2of silicon area. Compared to the recently published CMOS VCOs within the same frequency range, the proposed VCO simultaneously achieves a wide TR, low power dissipation, and low PN, resulting in a figure of merit (FOM) and FOM incorporating the TR (FOMT) of -187 and -193 dBc/Hz at 10-MHz offset from the center frequency, respectively.
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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) | 0.002 | 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".