Conceptual Design and Demonstration of an Automatic System for Extracting Switching Loss and Creating Data Library of Power Semiconductors
Bibliographic record
Abstract
A switching characterization (SC) test of power semiconductor devices (PSDs) gives us significant insight into the dynamic switching behavior of the device under various operating conditions. A double pulse test (DPT) is a widely used method for evaluating switching performance parameters of a PSD such as its switching losses, switching speed (di/dt, dv/dt), turn-on and turn-off times etc. The scientific information obtained from analysis of DPT results of a PSD helps in predicting its thermo-electric performance in a target power electronic converter. With conventional DPT setups, it is a time-consuming and error-prone process to manually conduct these tests under several permutations of test parameters and thereafter analyze the experimental data manually. This work presents a newly developed automated SC test system, which can run tests one after another, once the desired test parameters are entered in a graphic user interface. The test-control system also enables recording and systematic processing of the experimental switching data to deliver usable characterization results. The automatic, compact and modular design allows the proposed SC test platform to stand out from the conventional DPT setups. The design principles are experimentally verified by implementing a hardware prototype capable of testing PSDs up to 1000 V, 60 A, 250 °C.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".