A Programmatic Method For Selecting Transistors For High-Frequency Class-E Amplifiers
Bibliographic record
Abstract
Radiative wireless power transfer at high efficiency can be achieved with Class-D or Class-E amplifiers. These amplifiers have achievable efficiencies that can be very high but heavily depend on transistor selection. The objective of this work was to partially automate the selection process to quickly generate near-optimal Class-E amplifier designs entirely within MATLAB®. The candidate transistor SPICE models were parsed and converted into a system of first-order ordinary differential equations. A non-stiff numerical solver was used to find steady-state solutions for candidate circuits. A simplex search algorithm was used to find the maximum-efficiency candidate circuit for the transistor. The results were also simulated in LTspice®to confirm accuracy. Eighty-four transistors were compared and seven were found to have high efficiency capabilities at 2.45 GHz. The best performing device achieved 60.6% power-added efficiency in MATLAB®, and the same device achieved 53.0% power-added efficiency in LTSpice®. The incongruency between this work and LTSpice®was small enough to justify its use for eliminating large numbers of candidate transistors.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".