Performance Analysis of 4H-SiC Pseudo-D CMOS Inverter Circuits Employing Physical Charge Trapping Models
Bibliographic record
Abstract
For the analysis of the characteristics and behavior of circuits prior to fabrication and to improve circuit performance, simulations using Spice tools are typically performed. Such tools rely on static compact models describing the behavior of the individual circuit components such as transistors. In reality, the behavior of the transistors changes over time due to aging, for instance, as a consequence of bias temperature instabilities (BTI). BTI is typically referred to as a drift of the threshold voltage of a transistor due to charge trapping at oxide and interface defects. To explain BTI, power-law-like mathematical expressions are often employed. However, using these simple formulas, the experimental data can only be replicated with limited accuracy. To evaluate the performance of logic inverter circuits made from 4H-SiC CMOS transistors with high precision, we use a physics-based defect model to describe the change of the device behavior from a defect-centric perspective. Our results indicate the limitations of using power-law-like formulas as they lead to an overly pessimistic estimation for circuit parameters.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".