Zero-Power Feed-Forward Spur Cancelation for Supply-Regulated CMOS Ring PLLs
Bibliographic record
Abstract
A new reference-spur cancelation technique is presented for supply-regulated ring-oscillator-based integer-N phaselocked loops (PLLs). A passive RC filter is used to implement a feed-forward (FF) spur-coupling path to perform spur cancelation at the PLL control signal. The proposed technique achieves a simulated spur cancelation of about 22 dB at the first spur harmonic. The simulated postcancelation spur value is -79 dBc for an oscillator gain of 0.1 GHz/V and -46 dBc for an oscillator gain of 6 GHz/V. Spur cancelation is also robust against large process, voltage, and temperature variations in the gain and bandwidth of the FF path. A 1-GHz integerN PLL prototype in a 65-nm CMOS process has a measured cancelation of 19.5 and 13 dB at the first and the second spur harmonic, respectively, with 320 μW of total power consumption. The PLL prototype has an oscillator gain of 1.5 GHz/V, which results in a postcancelation spur of -53 dBc. The proposed zero-power technique is suitable for low-power PLLs as it achieves a large spur cancelation without requiring any additional power consumption or calibration.
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) | 0.002 | 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".