Development of a Device Characterization Curve Tracer for High Power Application
Bibliographic record
Abstract
Due to self-heating and significant temperature rise in a power device junction, the device characterization through the DC measurement is a major issue. Short pulsed technique or Pulsed I-V (PIV) characterization is the technique which is used by commercial curve tracer and network analyzers to characterize the power devices. Although, this technique prevent excessive self-heating but doesn't guarantee that measurement will be operated in the desired accuracy range because even a moderate self heating may cause significant measurement error. In this research work, a measurement technique is introduced that results "device characterization within the desired accuracy range". The technique is based on the stimulation of the device under test (DUT) with voltage ramps that allow for "fast transient mesurement". Because, this way of stimulation excites the parasitic impedances in the DUT, a dynamic model of the DUT is presented. This model allows determining the operation conditions that "guarantee the specified measurement accuracy". The measurement procedure is described and the developed measurement algorithms are implemented in LabVIEW environment to obtain a "PC-based device characterization curve tracer for high power application". A high current power MOSFET is used as the DUT. The calibration and measurement phases are carried out by the developed curve tracer. During the calibration phase, the measurement condition including allowed junction temperature deviation, maximum ramp slope and maximum allowed drain-source voltage to "guarantee 2% measurement error" is specified. The measurement phase is carried out based on these operating conditions. The result is a family of output I-V curves for different gate voltage set. This measurement technique "validated" with that of measured based on the PIV characterization technique from the device data sheet. The discrepancy between the measurement result and datasheet curve is discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".