Large-signal model generation of the dual-gate microwave MESFET from multi-bias S-parameter measurements
Bibliographic record
Abstract
In this paper we present a technique for automatically generating the large-signal lumped element model for the dual-gate MESFET. Values of the large-signal model are extracted from two-port S-parameter measurements at many DC-bias points. The automatic model generation is accomplished by integrating multiple software tools. The technique is tested on a 6-gate 1×100 µm dual-gate MESFET manufactured by Nortel Networks. The large-signal model is then verified through a variable gain large-signal amplifier application based on the dual-gate MESFET. The model is first imported to a commercial simulator. Harmonic balance simulations and experimental measurements of the verification circuit showed very good agreement of the first harmonic. For the second and third harmonic, some discrepancies between the measurements and the model are observed. This is mainly due to some model simplifications and second order effects.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".