High-Efficiency Input and Output Harmonically Engineered Power Amplifiers
Bibliographic record
Abstract
This paper presents an in-depth, systematic study of the impact of input and output harmonics in the design of high-efficiency power amplifiers (PAs). The study evaluates the performance of harmonically tuned amplifiers, tackling concurrently both input and output harmonics. The proposed theory starts with deriving an altered input voltage waveform under the impact of input nonlinearity. Intrinsic drain voltage and drain current components are formulated as a function of the conduction angle α considering both source and load terminations. Output power and drain efficiency are then computed as a function of input nonlinearity, α, and output loading conditions. The derived formulations allow to investigate the design sensitivity to input nonlinearity and its impact on fundamental design space. The impact of source harmonics is evaluated using harmonic source pull under different output loading conditions. Thereafter, PA design and implementation has been carried out using NXP 1.95 mm die to confirm the distinctive behavior of class GF and GF-1 amplifiers with respect to the input harmonic terminations. For practical validation, four different design cases with different second harmonic source impedances are investigated. At 2.6 GHz, drain efficiencies ranging between 76% and 83% were exhibited depending on the source and load harmonic tuning for each design case. Measurement results confirm the theoretical findings reported in this paper.
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.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".