Thermal diagnostics of high power electrical and optical device time to failure
Bibliographic record
Abstract
The authors discuss an optoelectronic system capable of providing information on the instantaneous spatially averaged junction temperature of high-power devices such as GTO (gate turn-off thyristors) and IGBTs (insulated-gate bipolar transistors) and high-power semiconductor lasers. The potential applications of such a system are threefold: to provide real-time information on the junction temperatures for control purposes, to use the information pertaining to the device temperature for purposes of understanding the mechanism for device failure, and to specify power device ratings. The key features of this system are a micromanipulated fiber cable, a collimator, an acoustooptic modulator, two cooled InSb detectors with associated narrow-band filters, a temperature sensor, an electronic processing unit, a thermoelectric and thermomagnetic cascaded cooling unit, and a switched mode power supply. Major design issues such as the reduction in 1/f noise and high bandwidth operation were addressed by using an acoustooptic modulator. The detectors were operated without bias in order to minimize the noise. The minimum internal noise was controlled by lowering the detector temperature until the noise level was background-limited.>
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".