A Top-Down Design Methodology Encompassing Components Variations Due to Wide-Range Operation in Frequency Synthesizer PLLs
Bibliographic record
Abstract
This paper presents a complete methodology to model, design, and implement wide tuning-range phase-locked loops (PLLs) using a top-down approach. Mathematical equations that illustrate the contribution of the different sources of noise in the PLL are presented. Behavioral models that encompass the nonidealities of the PLL components are described using Verilog-A language. The PLL components are designed, and the noise performance of each component is evaluated using transistor-level simulations. The extracted jitter from the individual blocks is used to find the overall system noise. The proposed methodology considers the variations in the loop dynamics due to changes in the voltage-controlled oscillator gain and noise, frequency divider ratio, and charge pump current. While optimizing the PLL for maximum tuning range, the methodology also considers the tradeoff between the noise, speed, and reference spurs attenuation. The design and implementation of an integer-N frequency synthesizer PLL that covers a continuous frequency range from 156.25 MHz to 10 GHz using a 65-nm CMOS technology is demonstrated in this paper. Measurement results to verify the accuracy of the models and to validate the predictions made by the simulations are provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".