Bibliographic record
Abstract
In this thesis a digitally controlled oscillator at 20 GHz is designed to have very fine tuning resolution.This high resolution enables the oscillator to change its frequency by a tiny fraction and in doing so improves the noise performance of a PLL, hence improving radio link quality.Following an introduction to the topic, and background study three designs were investigated to achieve fine resolution.The first design involves using the smallest kit varactor in the 130 µm CMOS technology in use available from IBM.This design achieves a measured resolution of about 30 MHz.Secondly a design involving the use of a small fixed capacitor in series with a larger varactor is used.In schematic simulation, this device achieved a resolution of 5 kHz.However due to the large size of the resonator covering digital tuning from 5 kHz to 2 GHz, the amount of interconnect greatly reduced the quality factor of the resonator and so the extracted view did not oscillate.Finally, in the third design, the same concept of the second design was used but this time only fine tuning was done digitally.This greatly reduced the size of the resonator and so the extracted view worked achieving a resolution of about 120 kHz at 20 GHz.After detailed simulation it became clear that the open loop gain in the oscillator was not sufficiently high enough and so any processing variations could lead to a further reduction of gain which would result in oscillations not starting up.For future designs, it is recommended that at such high frequencies, all passives in a circuit pass EM simulations before fabrication to obtain a better estimate of the expected on chip performance.Moreover, designs should pass slow corner simulations in order to make sure there is plenty of open loop gain, and in case there is not, measures should be taken to increase it.
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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".