Spontaneous Transistor Failures in Automotive Power Electronics
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The amount of electronics in vehicles is increasing, so is the amount of power electronics circuits, like inverters for electric motor drives or dc/dc converters. The muscles of these circuits are power transistors like MOSFETs and IGBTs - in each circuit are several of them.</div><div class="htmlview paragraph">While MOSFETs and IGBTs have advanced over the years in terms of their performance, their wide product spectrum and feature spectrum as well as cost, they are still not unbreakable, but semiconductors which are more sensitive to electrical or thermal overstress than, a relay for instance.</div><div class="htmlview paragraph">Especially electrical overstress, like overvoltage or over current, may damage a power transistor within a short time frame. Hence, electrical overstress must be avoided when designing the power electronics circuit.</div><div class="htmlview paragraph">However, even a power transistor in a carefully designed power electronics circuit, may still be exposed to over current, short circuit, over voltage, over temperature and so forth. This may damage the power transistor and may have severe consequences for the application. Unfortunately, power transistors do not have an intrinsic self protecting mechanism. Therefore, it is recommended to monitor voltage, current, temperature, compare against thresholds and turn off when needed.</div><div class="htmlview paragraph">This paper will discuss types of failures, common causes of failure, voltage and current waveforms in normal operation and failure modes and suggest methods to avoid the failure (operate the power transistor within safe boundaries). Alternatively, detection methods and an appropriate response strategy is given after a failure is detected.</div></div>
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".