Accurate Total Static Leakage Current Estimation in Transistor Stacks
Bibliographic record
Abstract
In this paper, a simple model for the estimation of static leakage current in NMOS transistor stacks is introduced. The three leakage mechanisms addressed are subthreshold leakage, gate-tunneling and gate induced drain leakage (GIDL). The algorithmic description of the model can be broken down into three phases i) pre-extraction , ii) estimation and iii) width scaling. In the pre-extraction phase, data necessary for subthreshold leakage estimation is extracted a priori. This also involves characterizing voltages required for a specific set of input vector scenarios (exception vectors/voltages). In the estimation phase, unit width GIDL and gate tunneling are estimated deterministically while subthreshold leakage is estimated using the pre-extracted data. Finally, in the width scaling phase each leakage component is then width scaled and summed, to give the total static leakage exhibited by the stack. The proposed model was scripted in MatLab and compared with SPICE simulations for various scenarios. The average total error for each scenario was under 3%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".