A 0.01mm<sup>2</sup> 4.6-to-5.6GHz sub-sampling type-I frequency synthesizer with −254dB FOM
Bibliographic record
Abstract
Power consumption, Performance in terms of phase noise and integrated jitter, and Area (PPA) are three design metrics that have driven countless research efforts in CMOS frequency-synthesizer design. Design limitations and system-level tradeoffs have made simultaneous optimizations of PPA metrics in PLLs challenging. In traditional Type-II charge-pump (CP) based PLLs, power is consumed in the VCO, divider (N), and CP to improve noise performance, and area is consumed in large loop-filter (LF) capacitors. ADPLLs are attractive due to compact LF, but are limited in noise performance. Sub-sampling (SS) PLLs eliminate divider noise, and remove the N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> amplification of the phase detector (PD), CP, and LF noise, thereby improving the overall phase-noise performance [1]. However, their area is large due to the LF capacitors [1]. The performance of CPs and ring-VCOs in traditional or SS Type-II PLLs are also encumbered by reduced V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DD</sub> in scaled CMOS processes. Overall, PLLs with ring-VCOs have higher noise [2,3], and PLLs with LC-VCOs have larger area [1]. Figure 15.6.1 highlights the PPA tradeoffs in prior art.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".