A GaN DC-DC Converter with In-Situ Detection of Aging-Induced C<sub>oss</sub> Changes for Device State-of-Health Determination
Bibliographic record
Abstract
Detecting power-device State-Of-Health (SOH) during converter operation can enhance system reliability by predicting imminent failure scenarios. While various aging indicators for GaN devices have been demonstrated in the literature, few are practically measurable in an active converter. This paper demonstrates that the large-signal device output capacitance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(C_{\text{oss}})$</tex> is a reliable indicator of short-circuit (SC) aging, and proposes an insitu measurement technique to capture its value by leveraging the operational waveforms of soft-switching converters. Experimental results show a 5% decrease in the large-signal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C_{\text{oss}}$</tex> after 5000 SC cycles, proving the usefulness of this parameter as an SOH indicator. The in-situ measurement technique is demonstrated in a synchronous buck converter operating in discontinuous conduction mode, successfully capturing the SC-aging-induced change in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C_{\text{oss}}$</tex> . The presented results and proposed measurement technique pave the way for system-level monitoring of power-device SOH and self-calibrating operation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".