A novel RF tunable impedance matching network for correcting the tested result deviation from simulated result
Bibliographic record
Abstract
In practical situation of designing a power amplifier, inaccurate model, tolerance of the components, manufacture tolerance, variation in substrate dielectric could make the measurement result different from simulated result. For correcting those errors, a novel tunable matching network with simple structure in load matching network is presented in this paper. Two variable capacitors are integrated in the load as a part of the matching network. The output impedance could be modified by changing the values of these two capacitors. By this method, 8 impedance points around the original simulated maximum PAE (Power added efficiency) point are chosen and measured to get the maximum PAE. A class B power amplifier at one-tone frequency 2.14 GHz with this structure is designed, fabricated and tested for demonstrating the method of correcting. The measurement result before correcting is different from the simulated one. After tuning these two capacitors in load matching network, the PAE is increased by 21.8% from 33.4% to 55.2%, which more agrees with the simulated results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".