A new and efficient CAD tool for model topology generation of RF/microwave transistors
Bibliographic record
Abstract
Transistor modeling is an essential process for circuits and systems design. It consists of two steps. The first is to propose a circuit topology. The second is to assign values to those components, by employing optimization techniques, to best fit experimental data. Most of microwave transistor models use a standard topology. Other topologies with different configurations have also been proposed in recent research works and in commercial software packages. To reach an optimum topology, tuning of the initial topology is required. This tuning process is usually based on a manual, trial-and-error procedure and is often specific to a given type of transistor. In this work, a new and efficient tool is introduced. It is able to automatically generate the most appropriate transistor topology as well as find its component values accurately. To illustrate the tool efficiency, experimental transistor data (S-parameters) was used. In this case the results show that the tool-generated topology fits the measured data better than the standard topology. The frequency range of the measurement data is 1 to 40 GHz. Compared to existing techniques, our approach is fully automated and requires minimal expertise from the external user. This point becomes critical when characterizing new components.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".