Enhanced empirical large-signal model for HBTs with performance comparable with physics-based models
Bibliographic record
Abstract
An accurate empirical large-signal model for an heterojunction bipolar transistor (HBT) is given. In the DC mode, thermal-dependent physics-based relations for Kirk and avalanche effects are included to improve the accuracy of the model. In the small-signal mode, in addition to the distribution of the base resistance and base collector junction, the model captures the variation of various AC parameters with both bias voltage and bias current over the entire forward-bias region and a wide range of signal frequencies. DC parameter extraction is easily carried out using suitable optimisation codes on the measured Ic–Vce curves and Gummel plots, whereas the AC parameters are determined from multibias S-parameter measurements. To assess the validity and the accuracy of the proposed model the empirical large-signal model is constructed for a 2×25 μm2 emitter-area transistor and compared with measurements in DC, small-signal and large-signal modes. The model is further tested by comparing it with the physics-based and well-established VBIC model. It is found that, despite its reduced complexity, the enhanced empirical model gives better agreement with measurements than the VBIC model in all modes of operation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".