Concurrent tri-band GaN HEMT power amplifier using resonators in both input and output matching networks
Bibliographic record
Abstract
This paper presents a novel method by using resonators in both input and output matching networks to design a tri-band GaN HEMT power amplifier. Two parallel resonators in series as one frequency selection element are used for each operation frequency. By applying this frequency selection element in both input and output matching networks constructed with microstrip line, tri-band matching network is realized. With our proposed frequency selection element, we can use the conventional L-type structure to design matching network for three frequencies so that the design analysis procedure is easier. We also propose a new simplified output matching network by using bias line to match the output impedance to reduce the number of resonators. To demonstrate our method, we fabricate a tri-band power amplifier that can work at 1 GHz, 1.5 GHz, and 2.5 GHz concurrently. Experimental results show that the output power is 39.8 dBm, 40.8 dBm, and 39.2 dBm with 56.4%, 58.3%, and 43.4% power added efficiency (PAE) at 1 GHz, 1.5 GHz and 2.5 GHz, respectively.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".