Knowledge-Based Extrapolation of Neural Network Model for Transistor Modeling
Bibliographic record
Abstract
Artificial neural network (ANN) is a useful technique for active device modeling. However, it shows limitations in the extrapolation region. To address this issue, we propose a novel knowledge-based neural network (KBNN) method. The KBNN technique consists of three submodels and their transition mechanisms. One submodel is a pure ANN model which is used for training data region. Two additional submodels are used for the extrapolation region. The proposed method ensures that the output and derivatives of ANN and extrapolation models match at the boundary of the measurement data. This keeps the KBNN model smooth and consistent, making it suitable for transistor design over a broad range. The precision, smoothness, and consistency of the proposed method are verified with a$2\times 250~\mu $m GaN HEMT device modeling. The results show that the KBNN model provides physically reasonable predictions over a wide extrapolation region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".