Design and Analysis of a Low-Voltage VCO: Reliability and Variability Performance
Bibliographic record
Abstract
This paper investigates an adaptive body biasing (ABB) circuit to improve the reliability and variability of a low-voltage inductor-capacitor (LC) voltage-controlled oscillator (VCO). The ABB circuit provides VCO resilience to process variability and reliability variation through the threshold voltage adjustment of VCO's transistors. Analytical equations considering the body bias effect are derived for the most important relations of the VCO and then the performance is verified using the post-layout simulation results. Under a 0.16% threshold voltage shift, the sensitivity of the normalized phase noise and transconductance of the VCO with the ABB circuit compared to the constant body bias (CBB) decreases by around 8.4 times and 3.1 times, respectively. Also, the sensitivity of the normalized phase noise and transconductance of the proposed VCO under 0.16% mobility variations decreases by around 1.5 times and 1.7 times compared to the CBB, respectively. The robustness of the VCO is also examined using process variation analysis through Monte Carlo and corner case simulations. The post-layout results in the 180 nm CMOS process indicate that the proposed VCO draws a power consumption of only 398 µW from a 0.6 V supply when the VCO frequency is 2.4 GHz. It achieves a phase noise of -123.19 dBc/Hz at a 1 MHz offset and provides a figure of merit (FoM) of -194.82 dBc/Hz.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".