Effect of Non-Ideal Cross-Sectional Shape on the Performance of Nanosheet-Based FETs
Bibliographic record
Abstract
In this article, the effects of non-ideal cross-sectional shapes of stacked nanosheet FET (NSFET) and nanosheet FET with inter-bridge channel (TreeFET) are studied through calibrated 3D TCAD simulations. The impact of non-ideal cross-sectional shapes on the electrical characteristics due to insufficient/excessive etch processes are investigated in terms of inner spacer (IS), nanosheet (NS) channel, and inter-bridge (IB) channel. Simulation results show that the geometry and material of the IS have significant effects on the performance of the NSFET. Compared with the rectangular inner spacer (RIS), the low-k crescent inner spacer (CIS) enhances the gate control capability while the high-k CIS degrades the drain-induced barrier lowering (DIBL) and reduces the gate capacitance (Cgg). The tapered NS channel improves short-channel effects (SCEs), but sacrifices the driving current. For the TreeFET, considering the fin angle and concave arc, the IB channel can degrade the gate control capability, and SCEs degradation is severe compared to the ideal structure. Therefore, the non-ideal cross-sectional shapes have a significant impact on NSFET-based structure. This research provides development guidelines for process and structure optimization in advanced transistor technology nodes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".