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<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup>. 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<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> 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<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup>, and the same device achieved 53.0% power-added efficiency in LTSpice<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup>. The incongruency between this work and LTSpice<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".