A 30–40 GHz fractional-N frequency synthesizer development using a Verilog-A high-level design methodology
Bibliographic record
Abstract
A design methodology for constructing a high-frequency (30 to 40 GHz) fractional-N synthesizer in a 65 nm TSMC CMOS process is presented. The method is focused on minimizing the phase noise at the output of the synthesizer while achieving the desired frequency range and frequency resolution. The method involves selecting initial values for each PLL component, simulating each using a transistor-level simulation, i.e. Spectre, and deriving a noise and linearity model of operation. Using an initial guess for the loop filter transfer function, together with a set of Verilog-A models for the various PLL components, the loop filter transfer function is adjusted so that the output phase noise behaviour is minimized. If the noise performance does not meet specifications, noise and linearity bounds on the individual PLL components can be derived. These, in turn, will force the re-design of all or some of the PLL components. The approach described here has been used to design a fractional-N synthesizer in the frequency range of 30 - 40 GHz with 5 MHz frequency steps having a phase noise of less than -90 dBc/Hz at a 1000 kHz frequency offset.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".