A Self-Adaptive Measurement System for IGBT Collector Current Using Package Parasitics
Bibliographic record
Abstract
Insulated gate bipolar transistor (IGBT) collector current measurement based on the package parasitics between the power emitter (E) and the Kelvin emitter (E') is a lossless and real-time monitoring method. A step to improve accuracy is to precisely match the time constant (Rf× Cf) of the measurement circuit against the package parasitics (LEE'/REE'). This article proposes an automated time constant matching method to realize the tuning of Rf× Cfto LEE'/REE', and a detection circuit to continuously measure the IGBT collector current using the package parasitics. In the proposed method, the Miller plateau voltage is first sampled, and a polynomial curve fit is carried out to determine the fitting parameters associated with the IGBT collector current (IC). A programmable resistor Rfis then adjusted automatically based on the step response of the readout voltage until the time constant is matched. A ratio between the readout voltage and a known ICis then calculated based on the Miller plateau voltage. The proposed system is verified using a field-programmable gate array controller and discrete components. Experimental results confirmed the functionality of the proposed method, showing that the readout voltage versus ICrelationship has good agreement in the range of 0-165 A, with less than 3% error. The proposed method can also be used for overcurrent protection with a detection time of less than 0.7 μs.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".