A Self-Injection <i>LC</i> Oscillator for Flicker Noise Reduction
Bibliographic record
Abstract
Self-injection has been used in lasers and photonic integrated circuits to reduce the laser’s phase noise (PN). We show that self-injection can be leveraged in GHzLCoscillators as well. Our oscillator employs a current-domain self-injection technique by leveraging second-harmonic extraction, capacitive phase shifting, and self-mixing through the oscillator’s bias path. The approach enables 90° phase-shifted injection completely on-chip, avoiding any bulky passive delay elements, and with only minor changes to the conventional class-BLCoscillators. Thus, the$1/f^{3}$corner can be reduced by at least an order of magnitude without any significant degradation in the tuning range or power consumption. Our proof-of-concept 4.6–6 GHz VCO in a 65 nm CMOS process achieves a$1/f^{3}$PN corner of 5–35 kHz, and a peak figure-of-merit (FoM) of 193 dBc/Hz, as well as a FoM normalized by the tuning range of 201 dBc/Hz. At 4.64 GHz, the oscillator consumes 1.45 mA from a 1$V$supply and achieves−78.6 and −141.3dBc/Hz PN at 10kHz and 10MHz offsets, respectively. Across the measured tuning range of 26%, the oscillator maintains an excellent FoM performance in both the$1/f^{2}$and$1/f^{3}$regions.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".