A Random Forest Model for Predicting and Analyzing the Performance of CNT TFET with Highly Doped Pockets
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Bibliographic record
Abstract
Abstract This paper presents a Random Forest (RF) machine learning model that relates the DC characteristics and high‐frequency response of a carbon nanotube (CNT) tunnel field‐effect transistor (TFET) with highly doped pockets to the transistor parameters. The analysis of multiple factors for a complex structure as the one studied here becomes expensive with the ordinary simulation techniques and hence machine learning (ML) offers a proficient method to model and enhance the understanding of the key factors that influence the CNT TFET with pockets in considerably reduced time. Numerical simulations are used to generate the data on which the model is trained. This dataset comprises ten input features and four output attributes. The tuned model is capable of predicting the output characteristics of the device with minimal mean squared error (MSE). The RF model is also compared to other ML algorithms to demonstrate its advantage.
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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