Parameters Identification of MESFET by Including Thermal Effect, Surface and Substrate Trappings Using Modified Hippopotamus Optimization
Bibliographic record
Abstract
Metal Semiconductor Field Effect Transistors (MESFETs) configured in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 H}$</tex>-SiC are utilized in various applications that impact our daily lives. The analysis of MESFET models under direct current characteristics is quite complex. This paper considers three main effects: surface trapping, thermal effects, and substrate trapping, to elucidate the direct current characteristics of the MESFET. The suggested model for the current-voltage (I-V) curves incorporates the Caughey-Thomas configuration. This configuration takes into account the trapping of electrons in the substrate through various deep-level trapping methods. Additionally, it addresses the field-dependent mobility of electrons and provides an analysis of the two-dimensional distribution of charge located beneath the gate. This comprehensive approach allows for a better understanding of the electronic behavior in the device under different operating conditions. This proposed method is comprehensive, accounting for all significant thermal effects and material flaws, including traps. Nonetheless, solving intricate mathematical equations of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 H}$</tex>-SiC MESFET to derive precise I-V characteristics across various regions is quite challenging due to its complexity. Consequently, the Hippopotamus Optimization Method (HOM) is employed to address a range of intricate equations related to 4H-SiC MESFET, thereby accurately deriving the I-V characteristics. Consequently, the performance of the suggested analytical model closely resembles that of a real-time <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 H - S i C}$</tex> MESFET. Various behavioral characteristics are obtained and evaluated using the MATLAB programming software.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".