On-Chip Dynamic Gate-Voltage Waveform Sampling in a 200-V GaN-on-SOI Power IC
Bibliographic record
Abstract
The continual improvement of GaN-on-Si processes motivates the integration of more complex circuits alongside GaN power devices. Additional transistors can be leveraged to provide control, logic, and protection; however, low-voltage GaN devices consume more power and area than similar CMOS counterparts. This article investigates the feasibility of a monolithic gate-monitoring circuit integrated with a GaN power device and gate driver. The monitoring circuit captures 16 samples within 50 ns during the gate rising transient and stores them in on-chip capacitors. The stored voltages are asynchronously read off-chip through integrated source-follower buffers and a digitally controlled multiplexer. The proposed design incorporates approximately 330 e-HEMT transistors and was fabricated in a 200-V GaN-on-SOI process. A detailed characterization was performed to calibrate the dynamic on-chip gate voltage from the sampled values that are read off-chip, paving the way for future active control based on this feedback. Experimental results and the postcalibration estimate of the on-chip gate voltage highlight that off-chip measurements are poor and pessimistic estimators for the on-chip dynamic excursions. The on-chip gate-voltage waveform was estimated using the sampling circuit while switching the power device at 80 V, 1.5 A, demonstrating more accurate measurements of on-chip signals. This circuit stands as a proof-of-concept for the viability of integrating relatively complex circuits in GaN power ICs to perform critical monitoring and sensing tasks.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".