Impact of Short-Wavelength and Long-Wavelength Line-Edge Roughness on the Variability of Ultrascaled FinFETs
Bibliographic record
Abstract
We examine the impact of line-edge roughness (LER) on the variability in the on-current and saturation threshold voltage of ultrascaled FinFET devices via quantum-mechanical transport simulation. We obtain a realistic model of LER by decomposing the LER into short-λ and long-λ fluctuations, and we consider their separate influences on device performance. We show that the long-λ fluctuations lead to greater device variability than the short-λ fluctuations, and we explain the difference between the two cases via the influence of fluctuating quantum confinement arising from the LER. Finally, we consider devices in which the long-λ fluctuations of the two fin edges are correlated and demonstrate that this correlation significantly improves the variability. Thus, we show the continued need for fabrication technology either to reduce the amplitude of the long-λ fluctuations or to ensure the long-λ fluctuations between the sidewalls of ultrascaled FinFET devices are correlated.
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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 | 0.000 | 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 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".