Simplified First-Pass Design of High-Efficiency Class-F<sup>−1</sup> Power Amplifiers Based on Second-Harmonic Minima
Bibliographic record
Abstract
This paper investigates the source and implication of efficiency minima that is typically observed during the second-harmonic load-pull of transistor amplifiers. The study starts with the theoretical derivation of time-domain voltage and current waveforms as a function of conduction angle (α) at the second-harmonic efficiency minima, where the output power and drain efficiency (DE) are at minimum. Thereafter, this paper unfolds a systematic re-engineering approach that is developed to recover the performance degradation and to exploit the region of efficiency minima in favor of design and implementation of high-efficiency inverse class-F power amplifiers (PAs). Interestingly, the inferences drawn from the in-depth analysis are shown to provide a simplified first-pass design approach that guarantees inverse class-F PA operation without an a priori knowledge of device parasitic elements. Theoretical postulations and simulation results are experimentally validated using an on-wafer active harmonic load-pull and a prototype design using 1.95-mm NXP gallium nitride die at a frequency of 2.6 GHz. The designed PA delivers an output power of 40 dBm with DE of 76% and gain of 12 dB at 3-dB gain compression. The measurement results confirm the theoretical framework reported in this paper.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".