Bibliographic record
Abstract
A systematic approach for synthesising gyrator-C active transformers using MOS transistors is presented. The topology of gyrator-C active inductors and their characteristics are briefly reviewed first. This is followed by the development of ideal gyrator-C active transformers, where only the capacitor loads of the transconductors synthesising active transformers are considered. The self and mutual inductances of both the primary and secondary windings of active transformers are investigated in detailed. Non-ideal gyrator-C active transformers are developed with the consideration of both the resistance and capacitance loads of transconductors. The intrinsic relation between the self and mutual inductances is derived. The configuration of gyrator-C active transformers with multiple primary and secondary windings is also developed. The proposed active transformers offer large and tunable self and mutual inductances with virtually no silicon area requirement. Several practical implementations of the proposed active transformers have been realised in TSMC-0.18 µm 1.8 V CMOS technology and analysed using SpectreRF with BSIM3v3 device models. Simulation results on voltage transfer characteristics, self and mutual inductances, quality factors, stability, the effect of process variations, and noise are presented. The application of the proposed active transformers is exemplified using a 1.6 GHz active transformer quadrature oscillator.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".