Concurrent tri-band GaN HEMT power amplifier using resonators in both input and output matching networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it