All‐digital power‐efficient integrating frequency difference‐to‐digital converter for GHz frequency‐locking
Bibliographic record
Abstract
This study presents an all‐digital power‐efficient integrating frequency difference‐to‐digital converter (iFDDC) and explores its applications in gigahertz (GHz) frequency‐locking. The iFDDC utilises a bi‐directional gated delay line (BDGDL) to detect and accumulate the frequency difference between two GHz signals and digitises the result with ultra‐low power consumption. The built‐in integration of the iFDDC ensures that the in‐band quantisation noise of the BDGDL and digital controlled oscillator (DCO) is first‐order suppressed. The all‐digital realisation of the iFDDC makes it fully compatible with technology scaling. The effectiveness of the proposed iFDDC is verified using the simulation results of a 5 GHz frequency‐locked loop designed in a Taiwan Semiconductor Manufacturing Company (TSMC) 65 nm 1.2 V complementary metal‐oxide‐semiconductor (CMOS). The iFDDC consumes only 474 µW, offering the lowest power/frequency efficiency among reported FDDCs. The DCO locks to 5 GHz reference in <10 cycles.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".