Bibliographic record
Abstract
This thesis presents the design and implementation of an active inductor using gallium nitride technology.This design is largely motivated by the lack of high-quality varactors in GaN.Active inductors are therefore proposed as an alternative to varactors as an impedance tuning mechanism in GaN which will act as the building block for tunable circuits such as VCOs, filters, phase shifters etc.The fabrication of such tunable circuits in GaN will in turn allow for highly integrated complete RF systems on chip in GaN.Furthermore, the design and implementation of a tunable bandpass filter with amplitude and frequency control making use of the aforementioned active inductor are presented.The proposed active inductor and filter were fabricated in a 0.5 μm pHEMT GaN process.The active inductor's measured tuning range at 3.5GHz is 8 to 20nH with a quality factor greater than 200.The measured tuning range of the active filter is 500 MHz with an S21 of 3dB.The active inductor occupies an active area of 350um by 175um.Fixed passive inductors of similar inductance occupy areas of approximately 350um by 350um (200% increase compared to active inductor area), while exhibiting extremely poor quality factors of less than 5.To the authors' knowledge, this is the first active inductor and bandpass filter implemented in GaN.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".