A 5.56 GHz Single Core Digitally-Controlled Oscillator With Direct Fine Tuning Steps of 2.85 kHz
Bibliographic record
Abstract
As nanotech gets smaller and decreases in size, it is aiding in giving rise to better and more robust smart devices. At the heart of most of these smart devices is some sort of an oscillator. The better the oscillator design and the higher its performance potential, the more robust, more complex, and smarter these devices can become. This work presents a single core DCO (digitally-controlled oscillator) that can step through fine tuning step sizes of as low as 2.85 kHz at frequency of 5.56 GHz without the need of using either dithering, dual -gm pair, inductive switching, expensive FinFET, silicon-on-insulator (SOI), or frequency division (pushing the sub-micron technology of regular 40 nm CMOS to another level). This small resolution is achieved by using fixed MOM caps (highest Q available capacitors in 40 nm TSMC kit). The DCO had 4 banksets on the drain with 16 banks each and 1 bankset on the source with 20 banks. The DCO spanned frequency of 5.41 GHz (all banks closed) to 6.75 GHz (all banks open) with a tuning range of 22.04%. The phase noise (PN) performance of the DCO was 114 dBc/Hz (@ 1MHz offset) at frequency of 6.75 GHz, and had FoM of -181 dBc/Hz @ 1 MHz offset; DCO power consumption was 10 mW.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".