A Fully Integrated 3.5-/4.9-GHz Dual-Band GaN MMIC Doherty Power Amplifier Based on Multi-Resonant Circuits
Bibliographic record
Abstract
In this article, a fully integrated dual-band gallium nitride (GaN) Doherty power amplifier (DPA) based on multi-resonant circuits (MRCs) is presented. A lumped T-type network in combination with the output capacitances of the main and auxiliary transistors is able to realize the quarter-wavelength transformer (QWT) in DPA, and its component values are totally decided by the operating frequency, given a certain characteristic impedance. MRCs are proposed to exhibit the required component values at two different frequencies. By replacing each component in the T-type network with an MRC, a dual-band QWT can be achieved. An integrated dual-band DPA is implemented in a commercial 0.25-$\mu \text{m}$GaN-HEMT process to validate the proposed method. The fabricated DPA shows a saturated output power of 43.8–44.7/44.4–44.8 dBm, a 6-dB back-off drain efficiency (DE) of 41%–46%/41.5%–47%, and a saturated DE of 49%–52%/56%–59% in 3.35–3.6/4.8–5.2 GHz, with a compact size of$2.8\times3.5$mm2. Applying a 100-MHz OFDM signal with a 7.8-dB peak-to-average power ratio (PAPR), an average efficiency of 37%/40% and an average power of 37/37 dBm are measured at 3.5/5.0 GHz, and the adjacent channel power ratio (ACPR) is better than −49 dBc after digital predistortion (DPD). To the best of our knowledge, the proposed DPA is the first demonstration of fully integrated dual-band GaN DPAs.
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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 | 0.000 |
| 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".