Process variation tolerant LC-VCO dedicated to ultra-low power biomedical RF circuits
Bibliographic record
Abstract
In this paper, a technique to mitigate the effect of process variations on the performances of a 1.830 GHz nano-scale CMOS LC-VCO is presented. The proposed complementary cross coupled LC-VCO, dedicated to low-power implantable RF microsystems, uses a linear voltage regulator to allow adaptive scaling of the VCO supply as a function of process parameters. The proposed VCO implementation has improved immunity to variations in phase noise and supply current caused by process variations, and hence avoids worst-case design. The LC-VCO was implemented using STMicroelectronics 1-V 90-nm CMOS process and simulated using SpectreRF to validate its performance. Compared with a identical LC-VCO powered using a fixed supply voltage, the average close-in phase noise is reduced by about 3.6-dB at 10 kHz offset, and the 3-¿ deviation is reduced from 3.53 dB to 0.48 dB at the same frequency offset. Furthermore, the average power consumption is reduced by about 40%, as is the 3-¿ deviation in current drawn.
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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 | 0.001 |
| 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".