Analytical Approach to Improve the Performance of a Fully Integrated Class-F Power Amplifier with 0.13 µm BiCMOS Technology Using Drain–Bulk Capacitor Modulation
Bibliographic record
Abstract
This article reports a novel technique based on drain–bulk capacitor modulation to improve the performance design of a class-F power amplifier (PA) used in low-power transceivers based on the I-Q amplitude modulation technique considering linearity–efficiency–miniaturization trade-offs. This idea is carried out by implementing a tuned capacitor in parallel with a cascode transistor on the output of the power stage to enhance the shape of the voltage–current amplitudes of the class-F PA by creating a new harmonic current component. Simulated results were obtained for the power back-off region of the proposed configuration, with an output power, power gain and power-added efficiency of 8 dBm (+ 5 dBm)B, 19 dB (+ 5 dB)B and 45% (+ 5% to 10%)B, respectively. In addition, post-layout simulations revealed a similar level of output power, a power gain of a 20 dB and a 28% power-added efficiency for an added capacitance equal to 1.3 pF. Class-F PA is implemented on a 732×605 μm2 chip’s surface. (B: indicates the improved values in the power back-off region).
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".