A 3.5 to 4.7-GHz Fractional-N ADPLL With a Low-Power Time-Interleaved GRO-TDC of 6.2-ps Resolution in 65-nm CMOS Process
Bibliographic record
Abstract
This paper proposes a low-power design method and a low-noise phase offset calibration technique for a gated ring-oscillator time-to-digital converter (GRO-TDC), which normally consumes a large percentage of most all-digital phase-locked loop (ADPLL) power. A single coarse counter logic structure along with time-interleaved even/odd paths significantly reduces the complexity and speed of the TDC logic. The proposed TDC consumes only 0.44 to 24 mW for 0.077 to 24.42 ns of detection range. The multi-path GRO accelerates the oscillation speed and achieves approximately 6.2 ps of time resolution. The GRO-TDC shows –1.43 to 1.35 least-significant bits (LSB) of differential non-linearity (DNL) and –1.32 to 1.96 LSB of integral non-linearity (INL) over a 11-bit dynamic range (DR). The entire ADPLL including the proposed TDC has been fabricated in a 65 nm CMOS process and occupies 0.67 mm2 of active area. The prototype ADPLL consumes 12.22 mW from 1.2 V supply and the TDC consumes only 0.65 mW for a 50-phase offset code. A modified integrating structure in the subsequent digital loop filter (DLF) has been developed to mitigate dithering noise on$V_{ctrl}$code and the measured reference spur is –69.38 dBc at 3.6 GHz center frequency. The tuning range of the implemented ADPLL is 3.5 to 4.7 GHz by using 2-bit band switching and 5-bit coarse control, while maintaining low-$K_{DCO}$values to suppress in-band quantization noise. The measured root-mean-square (RMS) jitter is 0.94 ps and 0.99 ps at 3.6 GHz integer-mode and 3.60743 GHz fractional-mode 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".