A Low-Offset VCO-Based Time-Domain Comparator Using a Phase Frequency Detector With Reduced Dead and Blind Zones
Bibliographic record
Abstract
We present in this paper a high-precision voltage-controlled oscillators (VCO)-based time-domain (TD) comparator. It involves two identical and linear VCOs to convert the input voltage difference of the comparator into the time/frequency difference. Also, it includes a novel low-power phase frequency detector (PFD) to compare the output frequencies of the VCOs. The proposed PFD technique reduces the problematic effects of missing edges and phase ambiguity in conventional circuits by minimizing dead-zone (DZ)/blind-zone (BZ) and suppressing unwanted output glitches. The TD-comparator prototype is fabricated in a 350 -nm CMOS process having an active area of 0.01 mm2. The comparator consumes 93.65 μ W power from a 3.3 -V supply and provides a conversion rate of 2.7 MHz with 148$\boldsymbol {\mu }\mathbf {V_{rms}}$input-referred noise. Measurement results of 5 fabricated chips show an input-referred offset standard deviation of 81.14${\mu }\text{V}$. Stand-alone characteristics measurements of the proposed PFD show a minimized DZ and BZ of less than 12 and 22.7 ps, respectively. With an almost${\pm 2\pi }$input phase range, the maximum operating frequency of the PFD is 1.32 GHz.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".