Analysis of integer-N phase-locked-loop architecture
Bibliographic record
Abstract
Phase-Locked Loops have a wide range of applications as frequency synthesizers. An analysis of Integer-N Phase-Locked-Loop (PLL) is presented in this article. The performance of a phase-locked loop is determined by a number of factors. It is shown whether good locking time performance can be provided by changing the loop bandwidth of the PLL system. The loop filter can be designed to accelerate the lock time because it controls the poles and zeros of the system. The PLL system is a typical Charge-Pump PLL which uses a Ring Voltage-Controlled Oscillator (Ring-VCO) to optimize the area as much as possible and to reduce power consumption. The phase noise of the Ring-VCO at 1MHz is -96.14dBc and the system can deliver a stable output frequency of 1.6GHz with a 100MHz reference clock. The Multi-Modulus Divider (MMD) used can provide divider ratios from 16 to 31. Simulation is done by TSMC 40nm process.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".