A Digital Phase-Locked Loop With Calibrated Coarse and Stochastic Fine TDC
Bibliographic record
Abstract
A coarse-fine time-to-digital converter (TDC) is presented with a calibrated coarse stage followed by a stochastic fine stage. On power-up, a calibration algorithm based on a code density test is used to minimize nonlinearities in the coarse TDC. By using a balanced mean method, the number of registers required for the calibration algorithm is reduced by 30%. Based upon the coarse TDC output, the appropriate clock signals are multiplexed into the stochastic fine TDC. The TDC is incorporated into a 1.99-2.5-GHz digital phase-locked loop (DPLL) in 0.13-μm CMOS. The DPLL consumes a total of 15.2 mW of which 4.4 mW are consumed in the TDC. Measurements show an in-band phase noise of -107 dBc/Hz which is equivalent to 4-ps TDC resolution, approximately an order of magnitude better than an inverter delay in this process technology. The integrated random jitter is 213 fs rms for a 2-GHz output carrier frequency with 700-kHz loop bandwidth. The calibration reduces worst-case spurs by 16 dB.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".