TopFinder: a new and efficient tool for topology construction and parameter extraction for RF/merowave transistors
Bibliographic record
Abstract
In this paper. a new tool for efficient topology construction of RFMcrowave transistors is proposed. This tool, called TopFinder (from Topology Finder), is able to determine the most appropriate RFMcrowave transistor topology as well as to extract the component values of this topology accurately. Starting from a set of S-parameter measurements as inputs, the tool constructs the optimum topology that best fits this set. The fmal obtained topology could be, in some cases, a simple variation of the widely used basic topology, known as the standard small-signal electrical equivalent circuit transistor topology. However, in most cases, the tool is able to produce an optimum equivalent circuit topology that is significantly different from the standard one. To demonstrate the effectiveness of the tool, we present two examples. In the fmt example, synthetic S-parameter data is generated from a previously know transistor topology and component values. The topology used in this example is slightly different from the standard one (simple variation). In the second example, actual transistor measurement data in the frequency range from 1 to 30 GHz is used. In this case the results show that the final topology fits well (up to 1 %) the measurement data. The produced topology is shown to he significantly different from the standard topology.
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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 | 0.000 | 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".