A Highly Linear GaN MMIC Doherty Power Amplifier Based on Phase Mismatch Induced AM–PM Compensation
Bibliographic record
Abstract
This article presents a highly linear Doherty power amplifier (DPA) based on phase mismatch. When output phase mismatch (OPM) is introduced, i.e., the phase shift of the output impedance transformer deviates from 90°, the power-combining network (PCN) will exhibit a certain amplitude-to-phase (AM–PM) characteristic. When input phase mismatch (IPM) is introduced, i.e., the main and auxiliary branches are not phase-aligned, the AM–PM of the PCN can be further finely tuned. By choosing proper OPM and IPM, the AM–PM of the overall DPA can be compensated by that of the PCN while maintaining reasonable back-off and saturated performances. Moreover, the PCN with phase mismatch shows gain expansion, and thus the amplitude-to-amplitude (AM–AM) distortion of the DPA can also be improved to some extent. A fully integrated DPA is implemented in a$0.25~\mu \text{m}$gallium nitride (GaN)-HEMT process to validate the proposed method. The fabricated DPA realizes an AM–PM of 2° and an AM–AM of 0.3 dB at 6.3 GHz, with a saturated power of 41.1 dBm and a 6 dB back-off drain efficiency (DE) of 45%. Applying a 200 MHz signal with a 7.8 dB peak-to-average power ratio (PAPR), a raw adjacent channel power ratio (ACPR) of −42 dBc and an average DE of 37.4% are measured at the output power of 33.1 dBm. When the carrier frequency is swept from 6.1 to 6.5 GHz, a raw ACPR below −39 dBc and an average DE better than 37% are maintained.
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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) | 0.001 | 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 | 0.000 | 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".