Accurate and Efficient Behavioral Modeling of GaN HEMTs Using An Optimized Light Gradient Boosting Machine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract An accurate, efficient, and improved Light Gradient Boosting Machine (LightGBM) based Small‐Signal Behavioral Modeling (SSBM) techniques are investigated and presented in this paper for Gallium Nitride High Electron Mobility Transistors (GaN HEMTs). GaN HEMTs grown on SiC, Si and diamond substrates of geometries 2 × 50 , 10 × 200 , and 4 × 125 , respectively are used in this study. A versatile set of LightGBM's hyperparameters including learning and tree specific parameters are meticulously optimized using a modern and vigorous optimization algorithm namely Osprey Optimization Algorithm (OOA) with the objective to accomplish superior model performance. The developed OOA‐LightGBM based models are validated for a wide array of operating conditions including for frequency values within a broad spectrum of 0.25 to 120 GHz, 0.1 to 26 GHz, and 0.1 to 40 GHz for GaN‐on‐SiC, GaN‐on‐Si, and GaN‐on‐Diamond HEMTs, respectively. The proposed SSBM techniques have demonstrated remarkable prediction ability and are impressively efficient for all the GaN HEMTs devices tested in this work.
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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 it