On the Origin of Holes During Polarization Reset in Floating Body Ferroelectric FETs Towards Improving Switching Efficiency
Bibliographic record
Abstract
In this work, we performed a comprehensive combined experimental and modeling study on the polarization reset mechanisms of floating body (i.e., channel) ferroelectric FETs, an important class of device with growing interests due to added functionalities and improved reliabilities. Using fully-depleted silicon-on-insulator (FDSOI) FeFET as a classical example, we demonstrate that: 1) without hole generation mechanisms, floating body FeFETs during reset is simply a capacitor divider, with negligible ferroelectric voltage drop for switching; ii) Band-to-band-tunneling (BTBT) around gate-to-S/D overlap even with zero drain bias generates holes to facilitate the reset in FDSOI FeFET, though at a slower speed and hold the reset state; iii) With scaling, S/D inner fringe field can enable fast reset, thus offering a potential efficiency boost approach; iv) a compact FDSOI FeFET model is developed that can capture the BTBT effect and reproduce the observed behaviors; v) the reset mechanism is also validated in a NAND string composed of FDSOI FeFETs, demonstrating its relevant applications. These insights show the strategies in improving reset efficiency, i.e., enhanced BTBT and inner fringe field.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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".