Physical modeling of forward conduction in IGBTs and diodes
Bibliographic record
Abstract
One of the main goals in the development of circuit-oriented physics-based models for power semiconductor devices is to make available to power electronics designers models that allow prediction of losses and switching waveforms for an arbitrary power converter application. Given the strong temperature dependence of device parameters and characteristics, a useful model must include a description of temperature and self-heating effects. A recently developed physics-based model for insulated-gate bipolar transistors (IGBT) and diodes has proven quite accurate for transient simulation under resistive and clamped inductive load conditions. In previous work the model validation has focused on diode reverse recovery and IGBT turn off with associated current tail over a wide temperature range. These phenomena are the greatest contributors to switching losses in power converters, so the proposed model has thus been proven capable of accurate switching loss predictions. Besides switching losses, the other significant contribution to semiconductor device losses is conduction loss. In this paper we investigate the behavior of diodes and IGBTs under forward conduction conditions over a wide temperature range between -125 and +125 /spl deg/C. Four different types of results are compared: experimental results, physics-based model results, finite clement simulation results, and analytical steady-state predictions to assess the accuracy of the physics-based model under forward conduction.
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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) | 0.001 | 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 | 0.001 | 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".